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The present invention provides a statistical modeling approach to automatic linguistic indexing of photographic images. The invention uses categorized images to train a dictionary of hundreds of statistical models each representing a concept. Images of any given concept are regarded as instances of a stochastic process that characterizes the concept. To measure the extent of association between an image and a textual description associated with a predefined concept, the likelihood of the occurrence of the image based on the characterizing stochastic process is computed. A high likelihood indicates a strong association between the textual description and the image. The invention utilizes two-dimensional multi-resolution hidden Markov models that demonstrate accuracy and high potential in linguistic indexing of photographic images.
1. A method for automatic linguistic indexing of images, said method comprising the steps of:
establishing a database of statistical models using a statistical modeling process, wherein the database is accessible by the computer, and each of the statistical models represents a predetermined semantic category;
associating a set of index terms with each statistical model in the database, wherein the set of index terms provides a description for the predetermined semantic category;
extracting a plurality of feature vectors from an image to be indexed;
statistically comparing the plurality of feature vectors extracted from the image to be indexed to the statistical models in the database;
determining a set of statistical models from the database that are statistically similar to the plurality of feature vectors extracted from the image to be indexed; and
extracting a set of statistically significant index terms from the descriptions of the set of statistical models and using the set of statistically significant indexed terms to index the image.
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13. A method of automatic linguistic indexing of images using a computer system, said method comprising the steps of:
generating a training image database having a plurality of training images wherein the plurality of training images represent at least one semantic category;
assigning a textual description to the at least one semantic category in the training database;
extracting a plurality of feature vectors from the training images using a statistical modeling process;
generating statistical models using the extracted feature vectors, wherein the statistical models are associated with portions of the textual description assigned to the at least one category and including a plurality of paths, each of the plurality of paths representing a variation of the training images;
storing the statistical models; and
using the stored models to index and assign a textual description to an image by statistically comparing the models with a plurality of features extracted from the image to be indexed to determine the statistical similarity between the image and each model.
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19. A computer system for use in automatic linguistic indexing of images, said system comprising:
a computer operative to receive an image to be indexed and assigned a textual description;
a plurality of different semantic categories disposed on said computer;
a statistical modeling algorithm operative to construct a statistical model representative of each one of said plurality of different semantic categories;
a database in communication with said computer for storing said plurality of different semantic categories and said statistical models, each of said different semantic categories and said statistical models having a predetermined textual description associated therewith;
a feature extraction algorithm operative to extract a plurality of feature vectors from the image to be indexed;
a feature comparison algorithm operative to statistically compare said plurality of feature vectors extracted from the image with each of said statistical models to determine statistical similarity between the image and each of said statistical models; and
a text assigning algorithm operative to extract a set of statistically significant index terms from said predetermined textual descriptions associated with said statistical models wherein said set of index terms provide the textual description for the image to be indexed.
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[0001] This application claims priority of U.S. Provisional Patent Application Ser. No. 60/561,391 filed Apr. 8, 2003, which is incorporated herein by reference.
[0002] The present invention relates to linguistic indexing of images. More particularly, the invention provides a system and method for automatic linguistic indexing of images that associates statistical models with semantic concepts wherein features extracted from images to be indexed are compared to the statistical models to facilitate the linguistic indexing process.
[0003] A picture is worth a thousand words. As human beings, we are able to tell a story from a picture based on what we have seen and what we have been taught. A 3-year old child is capable of building models of a substantial number of concepts and recognizing them using the learned models stored in his or her brain. Hence, from a technological stance, it is appreciated that a computer program may be adapted to learn a large collection of semantic concepts from 2-D or 3-D images, build models about these concepts, and recognize these concepts based on these models.
[0004] Automatic linguistic indexing of pictures is essentially important to content-based image retrieval and computer object recognition. It can potentially be applied to many areas including biomedicine, commerce, the military, education, digital libraries, and Web searching. Decades of research have shown that designing a generic computer algorithm that can learn concepts from images and automatically translate the content of images to linguistic terms is highly difficult. Much success has been achieved in recognizing a relatively small set of objects or concepts within specific domains.
[0005] Many content-based image retrieval (CBIR) systems have been developed. Most of the CBIR projects were aimed at general-purpose image indexing and retrieval systems that focused on searching images visually similar to the query image or a query sketch. These systems were not adapted to have the capability of assigning comprehensive textual descriptions automatically to pictures, i.e., linguistic indexing, because of the great difficulty in recognizing a large number of objects. However, this function is essential for linking images to text and consequently broadening the possible usages of an image database.
[0006] Many researchers have attempted to use machine learning techniques for image indexing and retrieval. One such system included a learning component wherein the system internally generated many segmentations or groupings of each image's regions based on different combinations of features, then learned which combinations best represented the semantic categories provided as examples by the user. The system required the supervised training of various parts of the image.
[0007] A growing trend in the field of image retrieval is to automate linguistic indexing of images by statistical classification methods to group images into rough semantic classes or categories, such as textured-nontextured, graph-photograph. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and by narrowing down the searching range in a database. The approach is limited because these classification methods are problem specific and do not extend straightforwardly.
[0008] Prior art methods for associating images explicitly with words have one major limitation in that the algorithm used with these methods relies on semantically meaningful segmentation, which is generally unavailable to image databases. Thus there is a need for a system and method of automatic linguistic indexing of images that overcomes the above disadvantage relative to segmentation and wherein the system provides scalability that allows for a large number of categories to be trained at once.
[0009] The present invention provides a system and method that automates linguistic indexing of images wherein categories of images, each corresponding to a distinct concept, are profiled and represented by statistical models. Each statistical model is thereafter associated with a textual description of the concept that it represents.
[0010] The method begins by establishing a database of statistical models using a statistical modeling process accessible by a computer wherein each of the statistical models represents a different semantic category.
[0011] The method advances with associating a set of index terms with one of each of the statistical models wherein the set of index terms operate to describe the semantic category represented by the statistical model.
[0012] The method advances by extracting a plurality of feature vectors from an image to be indexed and statistically comparing the plurality of feature vectors with the established statistical models.
[0013] The method continues with providing a set of statistical models that are determined to be statistically similar to the plurality of feature vectors extracted from the image to be indexed and then extracting a set of statistically significant index terms from the set of statistical models. The set of statistically significant indexed terms are assigned to the image thus providing the image with a description. One advantage of the present invention is that images are automatically indexed. Another advantage of the present invention is that it does not rely on semantically meaningful segmentation of images. A further advantage of the invention is that it allows large numbers of categories to be trained simultaneously. A still further advantage is that the invention allows for large numbers of images to be linguistically indexed simultaneously.
[0014] A better understanding of the present invention will be had upon reference to the following detailed description when read in conjunction with the accompanying drawings in which like parts are given like reference numerals and wherein:
[0015]FIG. 1 illustrates a diagrammatic view of the statistical linguistic indexing process for assigning a textual description to a test image as according to the invention;
[0016]FIG. 2 illustrates a diagrammatic view of the architecture for the statistical modeling process for developing statistical models of various categories for storage in a dictionary of categories as according to the invention;
[0017]FIG. 3 illustrates a diagrammatic view of the image hierarchy across resolutions assigned during the multi-resolution process as according to the invention;
[0018]FIG. 4 illustrates a diagrammatic view of the spatial relations among image pixels and across image resolutions that are considered during the statistical modeling process as according to the invention;
[0019]FIGS. 5A and 5B illustrate a diagrammatic view of distinct groups of training images used to train a given concept as according to the invention;
[0020]FIG. 6 illustrates a table of a plurality of distinct textual descriptions associated with the various categories of the training database as according to the invention;
[0021]FIG. 7 illustrates a percentage table which depicts the accuracy of the automatic linguistic imaging system as according to the invention;
[0022]FIG. 8 illustrates a histogram of the numbers of words assigned to test images as according to the invention;
[0023]FIGS. 9A-9C illustrate a diagrammatic view of three test images wherein 9A is annotated with only one word and FIGS. 9B and C are annotated with fourteen words each by using the automatic linguistic indexing process as according to the invention;
[0024]FIG. 10 illustrates a table of comparison between the performance of the present invention and a random annotation method of linguistic indexing of images;
[0025]FIGS. 11A-11D illustrate histograms of the coverage percentages obtained by the present invention and a random annotation method of linguistic indexing of images;
[0026]FIG. 12 illustrates a block diagrammatic view of the automatic linguistic indexing system components as according to the invention;
[0027]FIG. 13 illustrates a block a diagrammatic view of the automatic linguistic indexing method as according to the invention; and
[0028]FIG. 14 generally illustrates a process flow diagram of the automatic linguistic indexing method as according to the invention.
[0029] Referring to FIGS. 1-14, the present invention provides a computer system adapted to learn a large collection of semantic concepts using of a plurality of images, build statistical models about these concepts, and recognize these concepts based on the statistical models. Advantageously, the invention is operative to learn a plurality of images, build statistical models for the images, and recognize the images based on the statistical models of the images.
[0030] It should be appreciated that in this embodiment a statistical model is generated. As best illustrated in the block diagram of FIG. 13, the method of the present invention has three major components, the feature extraction process 50, the multi-resolution statistical modeling process 60, and the statistical linguistic indexing process 70. The feature extraction process involves using a statistical model.
[0031] Pictorial information of each image is summarized by a collection of feature vectors extracted at multiple resolutions and spatially arranged on a pyramid grid. A statistical model, preferably a two-dimensional multi-resolution hidden Markov model (2-D MHMM) is fitted to each image category wherein the model plays the role of extracting representative information about the images that make up the category.
[0032] In particular, the 2-D MHMM summarizes two types of information: clusters of feature vectors at multiple resolutions and the spatial relation between the clusters, both across and within resolutions. As a 2-D MHMM is estimated separately for each category, a new category of images added to the database can be profiled without repeating computation involved with learning from the existing categories. Since each image category in the training set can be manually annotated, a mapping between profiling 2-D MHMMs and sets of words can be established. For a test image, feature vectors on the pyramid grid are computed. The collection of the feature vectors are considered as an instance of a spatial statistical model. The likelihood of this instance being generated by each profiling 2-D MHMM is computed. To annotate the image, words are selected from those in the text description of the categories yielding highest likelihoods.
[0033] Many other statistical image models have been developed for various tasks in image processing and computer vision. However, because of its modeling efficiency and computational convenience, the present invention employs 2-D MHMMs as the statistical modeling approach to linguistic indexing. Although 2-D MHMMs are preferred for accomplishing the present invention, it is appreciated that other statistical modeling methods may be used without exceeding the scope as provided herein. More detail involving the feature extraction process is provided in blocks 51 through 54.
[0034] At block 51, multiple versions of the training image are obtained at different resolutions first. The original image corresponds to the highest resolution. Lower resolutions are generated by successively filtering out high frequency information. Wavelet transforms naturally provide low resolution images in the low frequency band.
[0035] Preferably, to save computation, features are often extracted from non-overlapping blocks in an image. An element in an image is therefore a block rather than a pixel. Features computed from one block at a particular resolution form a feature vector and are treated as multivariate data in the 2-D MHMM. The 2-D MHMM aims at describing statistical properties of the feature vectors and their spatial dependence. The numbers of blocks in both rows and columns reduce by half successively at each lower resolution. Obviously, a block at a lower resolution covers a spatially more global region of the image. As indicated by FIG. 3, a block at a lower resolution is referred to as a parent block, and the four blocks at the same spatial location at the higher resolution are referred to as child blocks. Such a �quad-tree� split is preferably always assumed in the sequel since the extension to other hierarchical structures is straightforward.
[0036] In the 2-D HMM, feature vectors are preferably generated by a Markov model that may change states once every block. Suppose there are M states, the state of block (i,j) being denoted by si,j. The feature vector of block (i,j) is ui,j. P(�) is used to represent the probability of an event. Denoting (I′,j′)<(i,j) if i′<i or i′=i,j′<j, in which case we say that block (i′,j′) is before block i, j. In this example, the first assumption is that
P(s i,j|context)=a m,n,l,
context={s i′,j′ ,u i′,j′:(i′,j′)<(i,j)},
[0037] where m=si−1,j, n=si,j−1, and l=si,j. The second assumption is that given every state, the feature vectors follow a Gaussian distribution. Once the state of a block is known, the feature vector is conditionally independent of information on other blocks. It should be appreciated that the covariance matrix Σs and the mean vector μ8 of the Gaussian distribution vary with state s.
[0038] The fact that only feature vectors are observable in a given image accounts for the name �Hidden� Markov Model. The state of a feature vector is conceptually similar to the cluster identity of a vector in unsupervised clustering. As with clustering, the state of a vector is not provided directly by the training data and hence needs to be estimated. In clustering, feature vectors are considered as independent samples from a given distribution. In the 2-D HMM, feature vectors are statistically dependent through the underlying states modeled by a Markov chain.
[0039] For the MHMM, the set of resolutions is denoted by
IN (r)={(i,j):0≦i<w/2R−r,0≦j<z/2R−r.
[0040] Images are represented by feature vectors at all the resolutions, denoted by ui,j (r), rε
[0041] At block 52, the system characterizes localized features of training images using wavelets. For example, in this process an image may be partitioned into small pixel blocks. The block size is chosen to be 4�4 as a compromise between the texture detail and the computation time. It is appreciated however that other similar block sizes can also be used.
[0042] At block 54, the system extracts a feature vector of six dimensions for each block. For example, three of these features are the average color components of pixels in the block, and the other three are texture features representing energy in high frequency bands of wavelet transforms. Specifically, each of the three features is the square root of the second order moment of wavelet coefficients in one of the three high frequency bands. The features are extracted using the LUV color space, where L encodes luminance, and U and V encode color information. The LUV color space is chosen because of its good perception correlation properties.
[0043] In this example, to extract the three texture features, either the Daubechies-4 wavelet transform or the Haar transform to the L component of the image may be applied. These two wavelet transforms have better localization properties and require less computation compared to Daubechies' wavelets with longer filters. After a one-level wavelet transform, a 4�4 block is decomposed into four frequency bands. Each band contains 2�2 coefficients. Without loss of generality, suppose the coefficients in the HL band are {ck,l,ck,l+1,ck+1,l,Ck+1,l+1}. One feature may then be computed as
[0044] The other two texture features are computed in a similar manner using the LH and HH bands, respectively.
[0045] It should be appreciated that these wavelet-based texture features provide a good compromise between computational complexity and effectiveness. It is known that moments of wavelet coefficients in various frequency bands can effectively discern local texture. Wavelet coefficients in different frequency bands signal variation in different directions. For example, the HL band reflects activities in the horizontal direction. A local texture of vertical strips thus has high energy in the HL band and low energy in the LH band.
[0046] In this example, the system automatically indexes images with linguistic terms based on statistical model comparison. FIG. 1 shows the statistical linguistic indexing process 10 as according to the present invention. For a given image I to be indexed, multi-resolution block-based features are first extracted by the same procedure 12 used to extract features for the training images.
[0047] To quantify the statistical similarity between an image I and a concept, the likelihood of the collection of feature vectors extracted from the image I is computed under the trained model 14, 14′, . . . 14 N for the concept. All the likelihoods 16, 16′, . . . 16 N, along with the stored textual descriptions 18 about the concepts, are analyzed by the significance processor 20 to find a small set of statistically significant index terms 22 about the image I. These index terms 22 are then stored with the image I in the image database 24 for future keyword-based query processing.
[0048]FIG. 2 illustrates the flow of the statistical modeling process 11. First, a series of concepts C, C′, . . . CN to be trained are manually developed for inclusion in a dictionary of concepts 24. Preferably, for each concept in this dictionary 24, a training set C, C′, . . . or CN containing images capturing the concept is prepared. Hence at the data level, a concept corresponds to a particular category of images. These images do not have to be visually similar. A short but informative description about any given concept in this dictionary is also manually prepared. Therefore, the present method has the potential to train a large collection of concepts because a description about each image in the training database does not need to be manually created.
[0049] Block-based features 30, 30′, . . . 30 N are extracted from each training image at several resolutions. The statistical modeling process 11 does not depend on a specific feature extraction algorithm. The same feature dimensionality is assumed for all blocks of pixels. A cross-scale statistical model about a concept is built using training images belonging to this concept, each characterized by a collection of multi-resolution features. This model is then associated with the textual description 18, 18′, . . . 18 N of the concept and stored in the concept dictionary 24.
[0050] The present invention focuses on building statistical models 14, 14′, . . . 14 N using images that are pre-categorized and annotated at a categorical level. If images representing new concepts or new images in existing concepts are added into the training database, only the statistical models for the involved concepts need to be trained or retrained. Hence, the system naturally has good scalability without invoking any extra mechanism to address the issue. The scalability enables the user to train a relatively large number of concepts at once.
[0051] With the statistical model described herein, spatial relations among image pixels and across image resolutions are both taken into consideration. This property is especially useful for images with special texture patterns. Moreover, the modeling approach does not require the segmenting of images and defining a similarity distance for any particular set of features. Likelihood may be used as a universal measure of similarity. Referring again to FIG. 13, the method from block 54 to block 60.
[0052] In block 60, the multi-resolution model is trained. The method advances from block 60 to block 62 for illustrating details of the multi-resolution modeling step 60.
[0053] To structure statistical dependence among resolutions, a first-order Markov chain is assumed across the resolutions. For example, given the states at the parent resolution, the states at the current resolution may be conditionally independent of the other preceding resolutions, so that
[0054] It should be appreciated that given its state si,j (r), a feature vector ui,j (r) at any resolution is conditionally independent of any other states and feature vectors. As the states are unobservable, during model estimation, different combinations of states may need to be considered. An important quantity to compute is the joint probability of a particular set of states and the feature vectors. Based on the assumptions, probability can be computed by the following chain rule:
[0055] At the coarsest resolution, r=1, and feature vectors are assumed to be generated by a single resolution 2-D HMM. At a higher resolution, the conditional distribution of a feature vector given its state is assumed to be Gaussian. The parameters of the Gaussian distribution depend upon the state at the particular resolution.
[0056] Given the states at resolution r−1, statistical dependence among blocks at the finer resolution r is constrained to sibling blocks (child blocks descended from the same parent block). Specifically, child blocks descended from different parent blocks are conditionally independent. In addition, given the state of a parent block, the states of its child blocks are independent of the states of their �uncle� blocks (non-parent blocks at the parent resolution). State transitions among sibling blocks are governed by the same Markovian property assumed for a single resolution 2-D HMM. The state transition probabilities, however, depend on the state of their parent block. To formulate these assumptions, denote the child blocks at resolution r of block (k,l) at resolution r−1 may be denoted by
DD(k,l)={(2k,2l), (2k+1,2l), (2k,2l+1), (2k+1,2l+1)}.
[0057] According to the assumptions,
[0058] where P{si,j (r):(i,j)εDD(k,l)|sk,l (r−1) can be evaluated by transition probabilities conditioned on sk,l (r), denoted by am,n,l(sk,l (r−1). Thus a different set of transition probabilities am,n,l is provided for every possible state in the parent resolution. The influence of previous resolutions may be exerted hierarchically through the probabilities of the states, which is best illustrated at 14 in FIGS. 1 and 2. The joint probability of states and feature vectors at all the resolutions in Eq. (1) may then be derived using
[0059] At block 62, the 2-D MHMM is estimated by the maximum likelihood criterion, such as by using an estimation algorithm (EM). The computational complexity of estimating the model depends on the number of states at each resolution and the size of the pyramid grid. Typically, the number of resolutions is 3; the number of states at the lowest resolution is 3; and those at the two higher resolutions are 4.
[0060] To summarize, a 2-D MHMM captures both the inter-scale and intra-scale statistical dependence. The inter-scale dependence is modeled by the Markov chain over resolutions. The intra-scale dependence is modeled by the HMM. At the coarsest resolution, feature vectors are assumed to be generated by a 2-D HMM. FIG. 4 illustrates the inter-scale and intra-scale dependencies model. At all the higher resolutions, feature vectors of sibling blocks may also be assumed to be generated by 2-D HMMs. The HMMs vary according to the states of parent blocks. Therefore, if the next coarser resolution has M states, then there are, correspondingly, M HMMs at the current resolution. Referring again to FIG. 13, the method advances from block 62 to block 64. At block 64, the trained model is assigned a textual description and stored in the system. Referring again to FIG. 13, the methodology advances to block 70.
[0061] In block 70, the system compares a given image statistically with the trained models in the concept dictionary and extracts the most statistically significant index terms to describe the image. The methodology advances to block 72.
[0062] In block 72, a collection of feature vectors at multiple resolutions
[0063] is computed in a given image.
[0064] is regarded as an instance of a stochastic process defined on a multi-resolution grid. At block 74, the similarity between the image and a category of images in the database is assessed, such as by using the log likelihood of this instance under the model M trained from images in the category, that is,
[0065] Preferably, a recursive algorithm is used to compute the above log likelihood. After determining the log likelihood of the image belonging to any category, the log likelihoods are sorted to find the few categories with the highest likelihoods at block 76. Suppose k top-ranked categories are used to generate annotation words for the query. The selection of k may be somewhat arbitrary. One example of an adaptive technique to decide k is to use categories with likelihoods exceeding a predetermined threshold. However, it is found that the range of likelihoods computed from a query image may vary greatly, depending on the category the image belongs to. A fixed threshold may not be useful. When there are a large number of categories in the database, it is observed that choosing a fixed number of top-ranked categories tends to yield relatively robust annotation.
[0066] At block 78, words in the description of the selected k categories are used as candidates for annotating the query image. If a short description for the query is desired, a certain mechanism may be required to choose a subset of words. There are many possibilities. The system may optionally provide multiple choices for selecting words with only negligible increase in computational load, especially in comparison with the amount of computation needed to obtain likelihoods and rank them. For example, suppose in the annotation of the k categories, a word appears j times. If the supposition that the k categories are chosen randomly based on the number of times the word arises is rejected, then confidence is gained in that the k categories are chosen because of similarity with the query. To reject the hypothesis, the probability of the word appearing at least j times in the annotation of k randomly selected categories is computed. A small probability indicates it is unlikely that the word has appeared simply by chance. Denote this probability by P(j,k) where:
[0067] and I(�) is an indicator function that equals 1 when the argument is true and 0 otherwise, n is the total number of image categories in the database, and m is the number of image categories that are annotated with the given word. The probability P(j,k) can be approximated as follows, using the binomial distribution if n,m>>k:
[0068] where p=m/n is the percentage of image categories in the database that are annotated with this word, or equivalently, the frequency of the word being used in annotation. A small value of P(j,k) indicates a high level of significance for a given word. Words are ranked within the description of the most likely categories according to their statistical significance. Most significant words are used to index the image at block 80 of FIG. 13, where statistically significant terms are assigned to the image to be indexed.
[0069] Intuitively, assessing the significance of a word by P(j,k) essentially quantifies how surprising it is to see the word. Words may have vastly different frequencies of being used to annotate image categories in a database. For instance, many more categories may be described by �landscape� than by �dessert�. Therefore, obtaining the word �dessert� in the top ranked categories matched to an image is in a sense more surprising than obtaining �landscape� since the word �landscape� may have a good chance of being selected even by random matching.
[0070] The proposed scheme of choosing words favors �rare� words. Hence, if the annotation is correct, it tends to provide relatively specific or interesting information about the query. On the other hand, the scheme is risky since it tens to avoid using words that fit a large number of image categories. The methodology ends at block 82.
[0071] An example of the present invention is described wherein the components of the ALIP system can be implemented and tested with a general-purpose image database including about 60,000 photographs. These images may be stored in JPEG format with size 384�256 or 256�384. The system may be written in the C programming language and compiled on two UNIX platforms: LINUX and Solaris. It is appreciated, however, that other programming languages may be utilizes for the intended purpose without exceeding the scope of the invention.
[0072] The system can be trained using a subset of 60,000 photographs based on 600 CD-ROMs, such as those published by COREL Corp. Typically, each COREL CD-ROM of about 100 images represents one distinct topic of interest. Images in the same CD-ROM are often not all visually similar. FIG. 5A shows images that may be used to train the concept of Paris/France with the description: �Paris, European, historical building, beach, landscape, water�. Images that can be used to train the concept male are shown in FIG. 5B. For the experiment, the dictionary of concepts contains all 600 concepts, each associated with one CD-ROM of images.
[0073] A set of keywords can be manually assigned to describe each CD-ROM collection of 100 photographs. The descriptions of these image collections range from as simple or low-level as �mushrooms� and �flowers� to as complex or high-level as �England, landscape, mountain, lake, European, people, historical building� and �battle, rural, people, guard, fight, grass�. On average, 3.6 keywords are preferably used to describe the content of each of the 600 image categories. FIG. 6 provides a table of example category descriptions.
[0074] While manually annotating categories, efforts to use words that properly describe nearly all, if not all images in one category, are preferably selected. It is possible that a small number of images may not described accurately by all words assigned to their category. These small number of images can be referred to as �outliers�, and introduced into training for the purpose of estimating the 2-D MHMM. There are ample statistical methods to suppress the adverse effect of them. On the other hand, keeping outliers in training can testify the robustness of a method. With the present invention, the number of parameters is small relative to the amount of training data. Hence the model estimation is not anticipated to be affected considerably by inaccurately annotated images.
[0075] To provide numerical results on the performance, the system can be evaluated based on a controlled subset of the COREL database, formed by 10 image categories including African people and villages, beach, buildings, buses, dinosaurs, elephants, flowers, horses, mountains and glaciers, and food, each containing 100 pictures. In further testing, categorization and annotation results may be provided with 600 categories. Because many of the 600 categories share semantic meanings, the categorization accuracy is conservative for evaluating the annotation performance. For example, if an image of the category with scenes in France is categorized wrongly into the category with European scenes, the system is still useful in many applications. Within this controlled database, annotation performance can be reliably assessed by categorization accuracy because the tested categories are distinct and share no description words.
[0076] Each concept may be trained using 40 images and tested on models using 500 images outside the training set. Instead of annotating the images, the program may be used to select the category with the highest likelihood for each test image. That is, the classification power of the system is an indication of the annotation accuracy. An image is considered to be annotated correctly if the computer predicts the true category the image belongs to. Although these image categories do not share annotation words, they may be semantically related. For example, both the �beach� and the �mountains and glaciers� categories contain images with rocks, sky, and trees. Therefore, the evaluation method used here only provides a lower bound for the annotation accuracy of the system. FIG. 7 shows the automatic classification result. Each row lists the percentage of images in one category classified to each of the 10 categories by the computer. Numbers on the diagonal show the classification accuracy for every category.
[0077] A statistical model is preferably trained for each of the 600 categories of images. Depending on the complexity of a category, the training process could take between 15 to 40 minutes of CPU time, with an average of 30 minutes, on an 800 MHz Pentium III PC to converge to a model. These models can be stored in a fashion similar to a dictionary or encyclopedia. The training process is entirely parallelizable because the model for each concept is estimated separately.
[0078] Illustratively, 4,630 test images outside the training image database may be randomly selected and processed using the linguistic indexing component of the system 10. For each of these test images, the computer program selects 5 concepts in the dictionary with the highest likelihoods of generating the image. For every word in the annotation of the 5 concepts, the value indicating its significance, is computed. The median of all these values is 0.0649. The median is used as a threshold to select annotation words from those assigned to the 5 matched concepts. In this case, a small value implies high significance. Hence a word with a value below the threshold is selected. For each word in the annotation of the 5 matched categories, a value indicating its significance is computed and thresholded by 0.0649.
[0079] A histogram of the numbers of words assigned to the test images is illustratively provided in FIG. 8. These numbers range from 1 to 14 with median 6. The unique image with only one word assigned to it is shown in FIG. 9(A). This image is automatically annotated by �fractal�, while the manual description of its category contains two words: �fractal� and �texture�. There are two images annotated with as many as 14 words, which are shown in FIGS. 9(B) and (C). For the first image, FIG. 9B, the manual annotation contains �mountain�, �snow�, �landscape�; and the automatically assigned words are �mountain�, �Rockies�, �snow�, �ice�, �glacier�, �sky�, �ski�, �winter�, �water�, �surf�, �up�, �boat�, �ship�, �no-fear�. The only word discarded by thresholding is �cloud� which would be a good description of the image although not included in the manual annotation. The value indicating its significance is 0.073, quite close to the threshold. Several words outside the manual annotation in fact describe the image quite accurately, e.g., �Rockies�, �glacier�, �sky�. This example shows that the computer annotation can sometimes be more specific than the manual annotation which tends to stay at a general level in order to summarize all the images in the category. For the second image, as shown in FIG. 9C, the manual annotation includes �season�, �landscape�, �autumn�, �people�, and �plant�. The word �autumn� used to annotate the category is not very appropriate for this particular image. The automatically annotated words have no overlap with the manual annotation. The word �people� is marginally discarded by thresholding. Other words assigned to this images include �sport�, �fitness�, �fight�. However, several words which may be assigned to this image may be about human activities, e.g., �sport�, �fitness�, �fight�, and hence imply the existence of �people�.
[0080] To quantitatively assess the performance, the accuracy of categorization for the randomly selected test images may be computed first, and then compared to the annotation system with a random annotation scheme. Although the ultimate goal of ALIP is to annotate images linguistically, presenting the accuracy of image categorization helps to understand how the categorization supports this goal. Due to the overlap of semantics among categories, it is important to evaluate the linguistic indexing capability. Because ALIP's linguistic indexing capability depends on a categorized training database and a categorization process, the choice of annotation words for the training image categories may improve the usefulness of the training database. The experimental results illustrated above show that both ALIP's image categorization process and linguistic indexing process can provide good accuracy.
[0081] The accuracy of categorization may be evaluated in the same manner above. In particular, for each test image, the category yielding the highest likelihood is identified. If the test image is included in this category, we call it a �match�. The total number of matches for the 4,630 test images is 550. That is, an accuracy of 11.88% is achieved. In contrast, if random drawing is used to categorize the images, the accuracy is only 0.17%. If the condition of a �match� is relaxed to having the true category covered by the highest ranked categories, the accuracy of ALIP increases to 17.06%, while the accuracy for the random scheme increases to 0.34%.
[0082]FIG. 10 illustratively shows the percentage of images whose true categories are included in their corresponding top-ranked k (k=1, 2, . . . , 5) categories in terms of likelihoods computed by ALIP. As a comparison, the number of categories required to cover the true category at the same accuracy using random selection is computed. When m categories are randomly selected from 600 categories, the probability that the true category is included in the m categories is
[0083] (derived from sampling without replacement). Therefore, to achieve an accuracy of 11.88% by the random scheme, 72 categories must be selected. FIG. 10 shows details about the comparison. Comparison between the image categorization performance of ALIP and that of a random selection scheme. Accuracy is the percentage of test images whose true categories are included in top-ranked categories. ALIP requires substantially fewer categories to achieve the same accuracy.
[0084] To compare with the random annotation scheme, all the words in the annotation of the 600 categories may be pooled to compute their frequencies of being used. The random scheme selects words independently according to the marginal distribution specified by the frequencies. To compare with words selected by our system using the 0.0649 threshold, 6 words may be randomly generated for each image. The number 6 is the median of the numbers of words selected for all the images by our system, and is considered as a fair value to use. The quality of a set of annotation words for a particular image is evaluated by the percentage of manually annotated words that are included in the set, referred to as the coverage percentage. It is appreciated that this way of evaluating the annotation performance may be pessimistic, because the system may provide accurate words that are not included in the manual annotation. An intelligent system tends to be punished more by the criterion, in comparison with a random scheme because among the words not matched with manually assigned ones, some may well be proper annotation. In this example, the mean coverage percentage is 21.63%, while that of the random scheme is 9.93%. If all the words in the annotation of the 5 matched concepts are assigned to a query image, the median of the numbers of words assigned to the test images is 12. The mean coverage percentage is 47.48%, while that obtained from assigning 12 words by the random scheme is 17.67%. The histograms of the coverage percentages obtained by our system with and without thresholding and the random scheme are compared in FIGS. 11A-11D.
[0085] One may suspect that the 4,630 test images, despite of being outside the training set, are rather similar to training images in the same categories, and hence are unrealistically well annotated. The annotation of 250 images, taken from 5 categories in the COREL database, using only models trained from the other 595 categories, i.e., no image in the same category as any of the 250 images is used in training, was examined. The mean coverage percentages obtained for these images with and without thresholding at 0.0649 are 23.20% and 48.50%, both slightly higher than the corresponding average values for the previous 4,630 test images. The mean coverage percentages achieved by randomly assigning 6 and 12 words to each image are 10.67% and 17.27%. It is thus demonstrated that for these 250 images, relying merely on models trained for other categories, the annotation result is at least as good as that of the large test set.
[0086] It takes an average of 20 minutes of CPU time to compute all the likelihoods of a test image under the models of the 600 concepts. The computation is highly parallelizable because processes to evaluate likelihoods given different models are independent. The average amount of CPU time to compute the likelihood under one model is only 2 seconds.
[0087] Referring now to FIG. 12, a block diagrammatic view of components of the automatic linguistic indexing system used to implement the methodology of FIGS. 13 and 14 is illustrated at 100. As illustrated, the system 100 preferably includes a computer 110 operative to receive images to be assigned a textual description. The computer essentially being comprised of a processor, a database, memory, I/O interfaces and a monitor. The input means for receiving such image may be illustratively provided via the download from an Internet connection, peripheral devices such as CD-ROMs or floppy drives, digital cameras, scanners, or the like.
[0088] An image database is provided in communication with the computer 110 for storing a plurality of different semantic categories wherein each category is preferably associated with a predetermined textual description that operates to describe at least a portion of the category it is associated with.
[0089] A second database 130 is provided in communication with the computer 110 for storing at least one statistical model that represents at least one semantic category disposed within the image database 120. Although not illustrated, a single database may be appropriately partitioned to store both the image database and statistical model database.
[0090] It is appreciated that the computer 110 is disposed with automatic linguistic indexing application software 140 for accomplishing the image indexing process as according to the invention. Essentially, the application software 140 includes a statistical modeling algorithm portion 142 operative to construct a statistical model representative of at least one of the different semantic categories disposed in the image database 120. The statistical modeling algorithm 142 preferably includes a feature extraction algorithm 150 operative to extract feature records from an image to be indexed as described above. Optionally, the feature extraction algorithm 150 may be provided independent of the statistical modeling algorithm however, without exceeding the scope of the invention.
[0091] The application software 140 also includes a feature comparison algorithm portion 160 operative to statistically compare features extracted from an image to be indexed with at least one statistical model disposed in the model database 130 to determine statistical similarities between the image and the statistical model stored in the database 130. The feature comparison algorithm 160 is further operative to extract a set of statistical models from the model database 130, wherein the set of statistical models extracted from the database 130 are the most similar to the features extracted from the image to be indexed.
[0092] Finally, a text assigning algorithm 170 is provided and operative to extract a set of statistically significant index terms from the predetermined textual descriptions associated with the set of statistical models extracted from the database 130. The set of index terms compiled by the text assigning algorithm 130 are operative to provide the textual description of the image to be indexed in the image database 120.
[0093] Referring now to FIG. 14, a process flow diagram for the automatic linguistic indexing system 10 is generally illustrated at 200. The training process is generally described in blocks 210 through 230. At 210, the process begins with providing an image database of different semantic categories wherein each category includes a plurality of images that relate to a distinct concept. The database may be provided as part of a local area network or at a remote location being provided in communication with a user interface via wireless or wire connections as known to those skilled in the art.
[0094] The process continues at 220 with establishing a database of statistical models using a statistical modeling algorithm as described above wherein the model is operative to represent at least one of the different concepts stored in the image database. Next at 230, a set of index terms is associated with each of the statistical models for providing a global description of the images that comprise the semantic category or distinct concept represented by the statistical model. After training is completed, the system 10 is primed for linguistic indexing of test images as generally described in blocks 240 through 270.
[0095] At 240 the process continues with the extraction of a plurality of multi-resolution block base features from the image to be indexed as described above. Preferably, the same statistical modeling process described above for establishing the database of statistical models at 220 is used as the means for extracting feature vectors from the image to be indexed.
[0096] Next, the plurality of features extracted from the image to be indexed are statistically compared to the statistical model stored in the model database 220 to determine the statistical similarities that exist between the image and each model within the database.
[0097] If a model is determined to be similar to the image to be indexed, the model is stored within a set of statistical models that are the most similar to the image to be indexed relative to the other models stored in the database 220 (see 260).
[0098] The system 10 operates to ensure that every established model in the database 220 is compared to the image to be indexed. After all models have been compared, a set of statistically significant index terms is extracted from the set of statistical models that were determined to be the most similar to the image to be indexed as described at 270. Accordingly, this set of index terms is thereafter associated with the image which can then become a part of the image database 210.
[0099] From the foregoing, it can be seen that the present invention provides a method and system to accomplish the automatic linguistic indexing of pictures by a statistical modeling approach. It is appreciated however that one skilled in the art upon reading the specification will appreciate changes and modifications that do not depart from the spirit of the invention as defined by the scope of the appended claims.
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Arizona Cardinals
Patrick Peterson gives Arizona Cardinals depth chart flexibility
Arizona Cardinals head coach Bruce Arians' depth chart for the team's Week 1 game against the St. Louis Rams features four wide receivers and six cornerbacks.
Larry Fitzgerald, Michael Floyd, Andre Roberts and Jaron Brown make up the group of wideouts, while the cornerbacks are Patrick Peterson, Jerraud Powers, Antoine Cason, Justin Bethel, Javier Arenas and Jamell Fleming.
But really, it's more like five wide receivers and six defensive backs, because Patrick Peterson plays both positions.
A third-year pro, Peterson spent a considerable amount of time this training camp working on his offensive game, and he's progressed to a point where the head coach does not see a need to add another "real" receiver to the roster.
"We practiced him a bunch, and being able to six corners, he can fill in a couple spots on offense," Arians said Monday.
So, the first-year head coach said the team is set at the WR spot, unless a "very special" player becomes available, and it's something Peterson feels is "pretty cool."
"I kind of teased Larry (Fitzgerald) today about it," Peterson said Monday. "He's like, 'technically, you're our fifth receiver.'"
Peterson views playing on both sides of the football as a great opportunity that will allow him to continue to grow as a player. But to play both sides, Peterson admitted he's had to adjust how he prepares for the season.
"I want to continue taking care of my body, my legs in general," he said. "Because at the end of the day, those are my money-makers.
"And especially being a defensive back going backwards and going to offense running forward, you can have a lot of hamstring injuries, so I want to make sure I take care of my lower half of my body and continue to get in the cold tub and doing all the necessary preparation I need to do to make sure that I'm ready for Sunday."
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Cardinals Audio
- Ron Jaworski, ESPN NFL analyst - Wednesday November 26ESPN NFL analyst Ron Jaworski says he believes Robert Griffin III has worn out his welcome in Washin
- Mark Schlereth- ESPN NFL analyst - Wednesday November 26Mark Schlereth discusses the importance of blocking both in the NFL and in the ESPN cafeteria.
- Story Time with Uncle Ron - Wednesday November 26Wolf draws parallels between Mike Smith's head coaching situation and his own head coaching situati
- John Clayton, ESPN NFL Insider - Wednesday November 26John Clayton analyzes the decisions that went into the benchings of Robert Griffin III and Michael V
- Wolf's Film Room - Wednesday November 26The Arizona Cardinals need to find their offense in Atlanta on Sunday.

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Showing 193-288 of 1,344 items
A perfect spot to enjoy excellent Italian cuisine in downtown Skokie. Brothers Ruben and Oscar serve all dishes made to order and the wine options combine perfectly with each dish. Pastas as well as meat and fish dishes round out a full range of entree options. Desserts should not be missed with the cannoli made fresh on site. Serving lunch and dinner.
CITY: Skokie
Rickshaw Rick's Tours & Taxi
Let us save your soles today! We'll take you for a ride, give you a tour, provide shuttles for your party or even run errands with our fleet of pedicabs (aka bicycle rickshaws). Call us at 773-771-3922 or visit www.rickshawrick.com. Catch us at the Oak Park Visitors Center, Frank Lloyd Wright Home & Studio, Write Inn, Harvey House, Under the Ginkgo Tree B & B and other fun spots.
CITY: Oak Park
45 North Barn & Grill
A unique, contemporary and nostalgic restaurant offering food that is created with heart and soul in a 'from-scratch' kitchen. Serving locally grown products, whenever possible, to create great meal choices. From hamburgers to steak, soup and salad, sandwiches, Friday night fish fry, and Sunday's Build Your Own Bloody Mary Bar, there is something for everyone to enjoy.
CITY: Antioch
Cabin Nature Center
Nature center, nature trail and dog park. Free admission, cost for some activities.
CITY: Wood Dale
Eclectic seafood restaurant in the French-American style. Ranked number one seafood restaurant by the prestigious Zagat Guide since 1993. See website for fabulous specials and promotions. Reservations recommended. Valet parking. Outdoor dining as weather permits.
CITY: Evanston
Koi Chinese and Sushi
Located right in the heart of charming Evanston's restaurant row, Koi brings the bold flavors of the eight different regions of authentic Chinese Cuisine and fresh sushi to every dining experience. The menu brings owner Sandy Chens' love for traditional Chinese cooking to the forefront.
CITY: Evanston
Adlai E. Stevenson Historic Home
Adlai Stevenson II was an important and influential figure in the political history of the United States. Stevenson was Governor of Illinois from 1949 to 1953 and ran twice for President as the Democratic National Candidate in 1952 and 1956. He also served as Ambassador to the United Nations from 1961 - 1965. The grounds are open daily for self-guided tours. The peaceful setting allows visitors to experience the historic landscape similar to when the family lived in the house. The house has been designated a National Historic Landmark. Group tours can be arranged through the Forest Preserves - 847-968-3422.
CITY: Mettawa
Lou Malnati's - Lincolnwood
Lou Malnati's, considered the oldest family name in Chicago pizza, has been creating rich thick slices of pie since 1971. People all over the state, the country and the world have heard of Chicago pizza, and with 36 locations, Lou Malnati's has been the ambassador of this heavenly creation.
CITY: Lincolnwood
Ernest Hemingway Museum
Explore displays featuring rare photos of Hemingway, his childhood diary, letters, early writings and other memorabilia. Enjoy videos documenting the author's life and work. Museum store on site.
CITY: Oak Park
Thai Sookdee
From Pad Thai to curry, Thai Sookdee offers a generous portions. Numerous vegetarian options. Delivery starts at 5pm.
CITY: Evanston
Ward Eight
An exceptional neighborhood cocktail bar. A menu of creative craft cocktails, unique wines and local beers is accentuated by a menu of small plates. Also offering retail sales, private event planning, and wine and spirits tasting classes.
CITY: Evanston
The Glen Club
Come for business, stay for golf. The Glen Club has 21 overnight rooms featuring unique layouts and elegant fixtures including historic photographs from Chicago's famous Golf Clubs. Dine on delicious American fare at "The Grill" or host your corporate events in our elegant ballroom seating up to 230, or our stately meeting rooms. Visit the Illinois Golf Hall of Fame. Designed by renowned golf course architect Tom Fazio, the Glen Club's golf course features rolling terrain with dramatic elevation changes, meandering lakes and streams and stunning vistas.
CITY: Glenview
Parkway by the Lake Banquets
For many years we have provided the best in wedding receptions at an affordable price. We take great pride in offering the best in service, the most picturesque setting for ceremony and reception, a huge dance floor and food to rave about for months after the event.
CITY: Ingleside
Steak 'n Shake - Evanston
This restaurant serves a full menu including chili, melts, salads, soups and club sandwiches. They are famous for their steak burgers.
CITY: Evanston
D & J Bistro
This chef owned since 1987 restaurant, offers a great line-up of "True Bistro Dishes", with special menu items available monthly. Offering Daily Specials available "A la Carte" or as a 3 Course Prix-Fixe Dinner. Gluten free, vegetarian and kids menus available. Private room for parties of up to 50 people.
CITY: Lake Zurich
Edzo's Burger Shop
This ubber-popular eatery brings a hand-crafted, quality approach to the iconic Chicago hot dog stand. Featuring freshly-ground burgers, hand-cut-fries, Chicago-style dogs and great shakes.
CITY: Evanston
Yolo's Mexican Eatery
Family owned and operated Mexican eatery located in Downtown Skokie. Yolo offers a remarkable eating experience and an eclectic menu that changes often and caters to a wide variety of tastes. Traditional Mexican food at its finest. "An absolute gem" rave reviewers on Yelp.
CITY: Skokie
Chili's Grill & Bar - Skokie
Chili's has sizzling fajitas, big mouth burgers and yummy desserts.
CITY: Skokie
Morning Glory
Creating beautiful fresh flower arrangements as well as blooming plants and European gardens. Work with our design team to create exquisite custom floral for weddings, parties, bar and bat mitzvahs and other special events.
CITY: Glenview
WinterFest in Downtown Oak Park
Join Oak Park for a cookie walk inside over 50 participating shops and restaurants! Catch a glimpse of Saint Nicholas, enjoy a variety of family friendly acts and warm up inside the hospitality tent with free hot chocolate and coffee.
CITY: Oak Park
Prairie Joe's
A traditional diner complete with a twist. Home-cooked foods and burgers, all set within the visually stimulating backdrop of art and ephemera for an eclectic diner experience.
CITY: Evanston
Oak Park Visitors Center
Visit us for maps, books, souvenirs and unique gifts. Tickets are available for Hemingway's Birthplace Home and Museum, Historic Pleasant Home and The Historical Society of Oak Park & River Forest. Purchase other seasonal special events tickets here.
CITY: Oak Park
Avli Restaurant
"Philoxenia" is the Greek word for traditional hospitality. Here at Avli everyone should feel like they are visiting an old friend. Traditional Greek dishes are done with passion for food.
CITY: Winnetka
Oberweis Dairy and Ice Cream Store
Oberweis is an old fashioned family ice cream parlor and dairy store. You can taste the difference in Oberweis milk, ice cream, cheeses, yogurts, cakes and pies. Dine in or take home and enjoy ice cream treats from the fountain menu for the entire family made from our super premium ice cream. Also enjoy the world famous shakes, malts, sundaes, smoothies, coffee drinks and more.
CITY: Gurnee
Jameson's Charhouse
Jameson's Charhouse has been serving quality steaks, chops and fresh fish for exceptional value for over two decades. Menu selections feature modern American cuisine full of delicious choices. All dishes prepared with the finest and freshest ingredients.
CITY: Skokie
Macks Golden Pheasant
Mack's Golden Pheasant Restaurant and Lounge has been serving fine food and cocktails since they were established in 1948. Situated in Elmhurst, Mack's Golden Pheasant is a very original restaurant. Many favor the lodge like atmosphere, and the food of course, which is particularly good. Visit and enjoy their history and unique ambiance.
CITY: Elmhurst
Golden Olympic Restaurant
This restaurant offers a large, traditional dinner menu with a particular emphasis on Greek specialties. Dine-in or carry out.
CITY: Evanston
Flight Restaurant and Wine Bar
Located in The Glen Town Center and recognized as one of the North Shore's most popular eateries, Flight's urban chic ambiance, extensive wine list and innovative menu (best described as American Contemporary Comfort food) will take your senses to greater heights. Customer favorites include the Duck Mac and Cheese, Pan Seared Scallops, Beef Tenderloin, Skirt Steak Flatbread, the Flight Burger and Herb Fries. Save room for dessert...the Chocolate Fondue paired with a Ruby Port is a must!
CITY: Glenview
Gusto Ristorante Italiano
Serving excellent Old World Italian cuisine. Featuring Tribune acclaimed award-winning eggplant parmigiana and chicken vesuvio. Combining home-cooked flavor with elegant sauces.
CITY: Glenview
Becky & Me Toys
Toys, dolls, games, books, crafts and more for infants to pre-teens. Our carefully-chosen toys encourage active, creative, open-ended play. Our knowledgeable staff can help you select perfect gifts for your special kids and for the kid in you.
CITY: Glenview
Yolo Mexican Eatery
Family owned and operated Yolo offers a menu that changes often and caters to a wide variety of tastes. Traditional Mexican food at its finest and a recent Bib Gourmand 2014 winner.
CITY: Skokie
Rachel Barton Pine at Ravinia
Called "the definition of the ideal musician for the 21st century" by Yo-Yo Ma, cellist Mike Block joins Rachel Barton Pine to chronicle the flow of music from Bach to Bartók to the blues. Bennett Gordon Hall Ticket Price: $10
CITY: Highland Park
Bagel Country
Bagel Country is the finest family-style dairy restaurants on the North Shore. Our 25 plus varieties of bagels and diverse menu is sure to offer something for everyone.
CITY: Skokie
Chipotle Mexican Grill
Enjoy the atmosphere and ethnicity of good, fresh food served at the counter. Specialties include burrittos and margaritas.
CITY: Evanston
Miller's Dog N Suds Drive-in
Original 50's style drive-in restaurant with car hops serving frosted mugs of Homemade root beer, root beer floats, Coney Dogs & Texasburgers on a tray at your car or enjoy our picnic area. The menu also contains delicious shakes and malts made to order. We feature summer Classic Car Cruise-Ins every Saturday. Check our web site for information and calendar of events.
CITY: Ingleside
Flat Top Grill - Evanston
Flat Top Grill is a create your own stir-fry restaurant. We offer more than 25 ingredients including traditional and exotic fruits and vegetables, a selection of rice and noodles, signature handcrafted sauces, prime beef tenderloin and seasonal offerings.
CITY: Evanston
Jameson's Charhouse
Jameson's Charhouse has been serving quality steaks, chops and fresh fish for exceptional value for over two decades. Menu selections feature modern American cuisine full of delicious choices. All dishes prepared with the finest and freshest ingredients.
CITY: Glenview
Siunik Armenian Grill
With passion for healthy, fresh and authentic Armenian cuisine, Siunik serves hummas, kabobs and Armenian tabuli like no other.
CITY: Skokie
Pleasant Home
Pleasant Home is an architectural gem that showcases 19th century craftsmanship and artistry. Designed in 1897 by prominent architect George W. Maher, the home is a National Historic Landmark and the only Maher building open to the public as a museum. You and your guests will be surrounded by rich custom woodwork, extraordinary art glass windows, intricate woodcarvings, and the glowing warmth of light from another era. The Pleasant Home's 1st floor includes of the Great Hall, Living and Dining Rooms and enclosed circular porch. The front porch is a great addition to the 1st floor during warmer weather. It is perfect for wedding ceremonies, smaller banquets or cocktail parties. The Library is slightly off the beaten path, but is a great place for a buffet station for a cocktail reception. It is also perfect for small lectures of meetings that do not require the use of the whole house.
CITY: Oak Park
E.J.'s Place
Great food, attentive service, and a comfortable north woods-inspired decor make EJ's Place a fine choice for lunch or dinner. The dinner menu leans toward red meat, fish and Italian specialties. Portions are enormous and doggy bags virtually de rigueur.
CITY: Skokie
Food that is comfortingly familiar, yet excitingly unexpected. Quince serves contemporary American cuisine in the heart of Evanston. Offering an outstanding dining experience in a lively and comfortable setting, all at an approachable price.
CITY: Evanston
Va Pensiero
Among the North Shore's finest Italian restaurants. Located inside the historic Margarita European Inn, the look here is decidedly Italy in the 1920's. The menu is seasonally inspired with an extensive wine list featuring a nice variety of reasonably priced Italian wines. During the summer, dining is available in the garden terrace.
CITY: Evanston
Tub Tim Thai Restaurant
A family-owned establishment with charm and coziness and very helpful waitstaff. Located in charming downtown Skokie, a trip to Tim Tub Thai offers an adventure of authentic Thai dining at its finest. The menu offers the usual Thai fare--Pad Thai, Tom Yum soup, beef salad -- all the standard Thai fare done superbly with other unique specialties.
CITY: Skokie
Sir Nick's Pizza
Great pizza.
CITY: Bartlett
The Curragh Irish Pub
A traditional Irish Pub in Downtown Skokie serving time-honored traditional Irish fare including Guinness Fish & Chips, Corned Beef & Cabbage and Shepherd's Pie just to name a few as well as pub favorites such as hamburgers and sandwiches. Smaller Pub Bites include the Hummus & Crispy Pita Plate, Spinach & Artichoke Dip and Chicken Quesadillas. Watch a game on their large TVs or enjoy a meal and a drink on the outdoor patio.
CITY: Skokie
Jet's Pizza
What are you hungry for today? Perhaps a pizza made with the freshest ingredients, a delicious sub sandwiched, deli boats, wings or original Jet's bread. Offering fresh salads such as antipasto, tuna, Greek and garden. Put a sweet ending to your meal with our awesome cinnamon stix!
CITY: Skokie
AOK Gourmet...on the go
From gourmet sandwiches and salads for five to catering for hundreds, we strive to make every experience unique and memorable. Enjoy sandwiches, salads, soups, entrees and more.
CITY: Skokie
Hilton Orrington/Evanston
This sophisticated North Shore hotel is a Four Diamond Award winner and a recipient of the 2012 Trip Advisor Certificate of Excellence Award. Located in the heart of downtown Evanston, The Hilton Orrington's focus is on superior guest service, and its 269 guest rooms join contemporary style and historic elegance. Enjoy updated modern amenities, including complimentary wireless high speed internet in all the public spaced as well as a variety of dining and lounge options, including Globe Café and the Indigo Lounge.
CITY: Evanston
International House of Pancakes - Skokie
Enjoy breakfast, lunch or dinner anytime!
CITY: Skokie
Denny's - Carpentersville
Serving breakfast, lunch, and dinner seven days a week!
CITY: Carpentersville
The Corner Chef
Corner Chef turns a casual meal into a special occasion. Our gourmet delivery menus offers a variety of boxed meals, warm and cold buffets and an assortment of hors d'oeuvres. We specialize in fresh, delicious food delivered on time.
CITY: Evanston
A small gallery that features unique mid-century pieces. Specialty furniture, lighting, and decor from the '50s, '60s, and '70s. Also offering mid-century art, posters, clocks, and mirrors, as well as quirky accessories.
CITY: Evanston
Prairie Moon
Offering seasonal regional American menu in an award-winning setting with two full bars, outdoor dining, and a large party room in the heart of Evanston. Lunch, dinner, and a late night grill are offered every day, along with breakfast on Sundays.
CITY: Evanston
Cucina Paradiso
Located off Oak Park's renowned Oak Park Avenue, specializing in classical and modern regional Italian cuisine. Begin your meal with a flavorful antipasto, followed by our famous thin pizzas, pastas, and tempting entrees. Voted by Chicago Magazine as"one of the best new restaurants of the year", and best Italian restaurant in Oak Park by Zagat Publications 2 years in a row. Preferred caterer for the Cheney Mansion and Pleasant Home. Exclusive caterer to 19th Century Club in Oak Park.
CITY: Oak Park
Days Inn O'Hare West
Business or pleasure, you will be treated to all Days Inn standards such as - Free WiFi high speed internet, free Daybreak Breakfast, Refreshing SolTerre shower experience.
CITY: Elk Grove Village
Flat Top Grill
Create your own stir-fry meal. Offering more than 25 ingredients including traditional and exotic fruits and vegetables, a selection of rice and noodles, signature handcrafted sauces, prime beef tenderloin and seasonal offerings including game and seafood. Also featuring Asian inspired appetizers, homemade desserts, and a full bar featuring Tiki drinks and urban cocktails.
CITY: Evanston
Glenview House
Extensively rehabbed and converted into an upscale American restaurant, bar and whiskey lounge, the Glenview House. Features beautifully presented, upscale American comfort food with French, Italian and Asian influences. The restaurant specializes in imported and craft beers with over 100 different selections available on draught and in bottles. Guests can enjoy lunch or dinner in one of the many dining areas. Seasonal outdoor seating available
CITY: Glenview
Coaches' Cafe
The perfect place for before, during or after the game. Menu features a variety of entrees, sandwiches and appetizers. Satellite TV in the bar. No better place to watch the game.
CITY: Evanston
Bracht's Place
Good food, good drinks, good company.
CITY: Bartlett
Celebrating the wonder and innocence of childhood, this charming children's boutique, located in the heart of the Chicago-Dempster Shopping District, is known for its selection of distinctive clothing, toys, books and gifts for boys and girls (newborn-10).
CITY: Evanston
Tommy Nevin's Pub
Featuring an all-day, Irish breakfast, hearty meals like Shepherd's Pie and Irish stew, plus healthier options such as raspberry balsamic salmon. There are 17 beers on tap, including Guiness and Harp. If the food and drinks don't provide sufficient Irish spirit for you, check out the daily drink specials.
CITY: Evanston
Mandarin Garden
Conviently located at the Central St. "El" station, Mandarin Garden offers classic Chinese dishes.
CITY: Evanston
Merle's Barbecue
Meats are slow-smoked using the greenest hickory wood to ensure the juiciest, most favorable taste. To complement that, a variety of homemade barbeque sauces and other Southern specialities including salads and seafood dishes are offered.
CITY: Evanston
Taco Diablo
An auténtico Mexican taqueria and cantina with rustic, art filled decor. Located in Downtown Evanston only steps from Pace/Metra/CTA Purple lines. Eat Tacos-Drink Tequila, Buen Provecho!
CITY: Evanston
Tatami Sushi
Whether you seek familiar sushi preparations, traditional Japanese cooking or unique and innovative contemporary cuisine that blends Asian and Western cooking styles, their seasonal cuisine, comfortable décor and knowledgeable service will provide you and your guests a dining experience that is both entirely satisfying and highly memorable. Come experience the creativity of urban Japanese cuisine in a soothing ambience by the day and upbeat atmosphere by night. Although sushi is their forte, there are plenty of cooked entrees and appetizers from the kitchen.
CITY: Glenview
Oregano's Corner Café
Oregano's serves Italian comfort food, using the same old country recipes passed down for over 100 years - one generation to the next. Only the freshest ingredients available are used, and of course, each dish is prepared the old fashioned way - from scratch order by order, so it does take just a little longer to serve you. Come hungry, enjoy, take home what you don't finish and leave happy!
CITY: Hawthorn Woods
Riccardo's Restaurant
Riccardo's Ristorante is a wonderful Italian restaurant with an amazing array of delicious meals. Old-fashioned Italian menu items and daily imaginative specials are sure to please everyone. Menu items are rich and fulfilling. Great place for family get-togethers or grabbing a pizza with friends.
CITY: Schaumburg
Globe Cafe and Bar
Geared to today's business professional and educated travelers who want to keep up with world events. This friendly neighborhood restaurant offers newspapers and magazines, flat screen TVs and media towers located throughout the café, along with high-speed wireless internet. Plus, really great food.
CITY: Evanston
Sisters Salon
The Sisters Jeanette & Elaine have been practicing the art of waxing for over 30 years and certainly love what they do. Friendly estheticians were trained by the Sisters and regularly update their techniques to give you the best services anywhere. Sisters invite you to come Discover Yourself and experience our one of a kind services for yourself.
CITY: Skokie
Pizano's Pizza and Pasta
Featuring full-service bar, Pizano's also has 18 high definition televisions for your sports viewing pleasure. Voted top 10 pizza in the U.S. by USA Today and #1 thin-crust pizza by the Oprah Winfrey Show.
CITY: Glenview
Las Palmas of Evanston
Come and enjoy delicious authentic Mexican fare at a very reasonable price. Stop by Las Palmas for a taste of Mexico.
CITY: Evanston
Wildberry Pancakes & Cafe
Wildberry specializes in gourmet breakfasts, fresh baked breads, panini sandwiches and so much more. Enjoy the inviting and warm fireplace or our beautiful outdoor patio. We also have a private dining room with beautiful French doors and mahogany stained hardwood. Our well trained staff is devoted to you and is always ready to help you out with anything you might need.
CITY: Schaumburg
Dame Couture
This custom dress shop specializes in wedding gowns but also makes cocktail and eveningwear for women of all ages and taste. Dame also restyles and reconstructs vintage bridal. The shop's charming vintage atmosphere makes a lovely backdrop for a selection of jewelry by local artisans as well as veils, bags and a few off-the-rack dresses.
CITY: Evanston
Bar Bordeaux
Offering an authentic French dining experience unlike any other amid the bustle of downtown Evanston. The wine list features a wide selection of reasonably priced French wines. Serving dinner seven days a week and brunch on Sunday. Winner of the Critic's Choice Award from Time Out Chicago Magazine.
CITY: Evanston
Hyatt Place Chicago/Itasca
Contemporary decor with stylish furnishings including divided living and sleeping areas, two double plush Hyatt Grand Beds™, state-of-the-art media and work center with a 42" flat-panel high-definition television that easily integrates with laptops and other electronic devices, an over-sized Cozy Corner sofa-sleeper, wet/dry bar and bathroom vanity with granite counter tops. Enjoy complimentary hotel-wide WiFi and continental breakfast buffet.
CITY: Itasca
Perspective Group and Photography Gallery
A community-oriented, not-for-profit cooperative that promotes fine art photography through exhibitions, critiques, workshops and outreach activities. The gallery represents the work of fine art photographers from the Chicagoland area, and sponsors LENS, an international, juried photography show on an annual basis.
CITY: Evanston
Ernest Hemingway Birthplace
Visit the Victorian home where Nobel Prize winning author Ernest Hemingway was born in 1899 and spent his early life.
CITY: Oak Park
Charcoal Oven
Charcoal Oven offers a wide variety of fresh fish, steaks, and Greek specialites daily.
CITY: Skokie
Firehouse Grill
Located in a refurbished fire station, the Firehouse Grill features a variety of entrees, sandwiches, burgers and appetizers. Excellent beer selection.
CITY: Evanston
Celtic Knot Public House
An authentic public house with excellent food, well-poured pints and a worldwide wine list. Regular live music includes Bluegrass Mondays, Celtic sessions Tuesdays, afternoon tea Wednesdays and Saturdays; storytelling monthly.
CITY: Evanston
China Buffet - Carpentersville
Great Chinese food - wide variety buffet.
CITY: Carpentersville
Morgan Christopher Salon and Spa
Hair and nail service, waxing and make-up, massage.
CITY: Bartlett
At Benihana your food is not only cooked - it's choreographed. Our chefs will have you craving for more as they slice and dice your meal right before your eyes. Highly skilled and well-trained, your personal chef will entertain you as they cook such favorites as steak, chicken, seafood and fresh vegetables in traditional Japanese style on a hibachi table. So, come in today and witness first hand what has become the longest running dinner show in the world. Reservations are recommended.
CITY: Schaumburg
The Book Stall at Chestnut Court
A North Shore institution, The Book Stall is one of the leading independent bookstores in the Chicagoland area. This general trade book store has a reputation for trustworthy customer service, numerous author appearances throughout the year, and a highly popular monthly newsletter with a distribution of 8,500.
CITY: Winnetka
Pensiero Ristorante
Among the North Shore's finest Italian restaurants tucked inside the historic Margarita European Inn. Pensiero Ristorante serves Italian fare prepared from the finest imported ingredients. The moment you enter the room, you know you are in for a special dining experience. The menu is seasonally inspired and the wine list is sure to compliment your dining experience. During the summer, dine outdoors in the lovely garden terrace.
CITY: Evanston
48th Annual Elmhurst College Jazz Festival
Chicago magazine once hailed the Elmhurst College Jazz Festival as "one of the best buys for your jazz dollars," and its easy to see why. Each February, the best college jazz bands in the country converge on the Elmhurst campus for three days of performances and education. The bands take turns performing for some of the greatest names in professional jazz today, who offer critiques and award a variety of honors. The professionals cap off each night of the Festival with a rousing performance of their own.
CITY: Elmhurst
Greenline Wheels
Greenline Wheels rents ecologically friendly and affordable transportation - bikes, electric bikes, and neighborhood electric vehicles. Easy to use maps with self-guided tour loops and easy access to many of Oak Park's independent tour guides makes Greenline an ideal starting point.
CITY: Oak Park
Johnny Rockets
Johny Rockets is a place where you can sit in a red-padded booth, play your favorite song and eat some good old-fashioned hamburgers and more.
CITY: Skokie
Saluto's Pizza & Pasta
Saluto's, a favorite Italian restaurant in Gurnee features a two-story bar, a large dining room with booths and a private banquet meeting room. Famous for their homemade pasta and the "secret recipe" salad dressing, Saluto's has been a hit in northern Lake County since 1990. This full service restaurant's claim to fame is the pizza, pasta and the courteous service. From their delectable appetizers like Artichoke Heart Fondue, Toasted Ravioli and so much more; to their homemade sauces and pastas; to their pizza, either thin, pan or stuffed, everyone will find something to enjoy on their menu. Very family oriented, Saluto's also offers a children's menu for the smaller guests.
CITY: Gurnee
Shula's Steak House
Offers fresh seafood, lamb and live Maine lobster. Relax in the "No Name Lounge" for a martini.
CITY: Itasca
Pat's Place
Pat's Place is a traditional diner in every sense with traditional home cooking, great prices and of course friendly service. Newly remodeled.
CITY: Skokie
Sundance Saloon
This multi-use country/western establishment provides a unique experience for those who like to dance and have fun, while also providing services and catering for businesses, conventions, meetings and special events.
CITY: Waukegan
Midlane Golf Resort
With five elegant banquet rooms that serve the needs of groups from thirty to six hundred guests including a championship eighteen hole golf course, driving range, all suites hotel with a pool/ fitness center, and a state-of-the-art nightclub, Midlane is uniquely outfitted to suit any of your banquet needs. Don't gamble on the outcome of your special event - come to the golf, banquet and wedding experts.
CITY: Wadsworth
Le Peep Pancake House & Grill
All your breakfast favorites in a relaxing and comfortable environment.
CITY: Evanston
El Molino
Serving great Mexican food.
CITY: Carpentersville
Days Inn - Libertyville
Close to Great Lakes, Six Flags Great America, and Gurnee Mills. We also offer a deluxe continental breakfast. www.The.DaysInn.com/Libertyville13906
CITY: Libertyville


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14 results for "ncaa football"
USC takes over control of Los Angeles Memorial ColiseumArash Markazi
After years of public and private negotiations, USC finally has control over the Los Angeles Memorial Coliseum for the next century.Story | Conversation | September 05, 2013
Max Redfield comes from long line of athletesBrandon Oliver
Safety prospect Max Redfield has plenty of athletic examples in his family from watch to follow to success.Story | Conversation | May 30, 2012
Haiti humanitarian trip a life-changing experience for USC Trojan...Garry Paskwietz
Sixteen USC players took a life-changing trip to Haiti to help impoverished natives in that country. In the process, they learned something new about themselves.Story | Conversation | May 22, 2012
How to make the Coliseum shine USC Trojans styleGreg Katz
With USC almost set to take control of the Coliseum, Greg Katz has a few suggestions on how to add some Trojans pizazz to the legendary landmark.Story | Conversation | January 19, 2012
Is Oregon a 'genius' program?Ted Miller
The Wall Street Journal wonders if it's time to induct coach Chip Kelly and his Oregon Ducks into the "Hall of Geniuses." In a potentially related item: Thousands of heads spontaneously exploded throughout Seattle and Corvallis. The gist of the...Blog | May 05, 2011
College Football: Bruce Feldman explains his early preseason top ...Bruce Feldman
Bruce Feldman explains his early preseason top 10 rankings, which feature the Oklahoma Sooners at the top and some surprises rounding out the list.Blog | Conversation | April 13, 2011
Our candidates for LA athlete of year: Kobe Bryant, Landon Donova...Arash Markazi
These athletes starred in L.A., even when some of their teams didn't.Story | Conversation | December 28, 2010
Hindsight is 2010: Writers discuss their most memorable stories
Hindsight is 2010: Writers discuss their most memorable stories.Story | Conversation | December 27, 2010
USC Trojans in talks to buy Los Angeles Memorial Coliseum propert...Ramona Shelburne
USC is in talks with California officials to buy the property where the Los Angeles Memorial Coliseum and the Los Angeles Sports Arena are situated.Story | Conversation | December 08, 2010
Taylor Martinez Heisman talk takes thunder from Denard RobinsonBruce Feldman
After proving he's a top QB threat against Kansas State, Nebraska's Taylor Martinez is a top-five candidate for the Heisman. Plus how the national-title picture looks as of this week, trouble for Dennis Erickson and what Texas can learn from UCLA.Blog | Conversation | October 08, 2010
Pat Forde kicks off the season by introducing us to the most inte...Pat Forde
The first Forde-Yard Dash of the 2010 season sprints through the game's most interesting men, biggest dolts and name-drops a few game-changers.Story | Conversation | August 30, 2010
Lane Kiffin, Pat Haden, J.K. McKay start to dig USC out of deep d...Ivan Maisel
The Pat Haden/J.K.McKay band is back together at USC. Lane Kiffin is their new frontman. Can Troy's trio find the right sound and produce gridiron hits now and in the future? It won't be easy.Story | Conversation | August 23, 2010
Top sports searches of 2009 - Top searches of the yearMaalek Marshall
ESPN.com ranks the top searches of the year for 2009.Story | Conversation | December 31, 2009
No game, but plenty of politics at White HouseIvan Maisel
President Bush asked USC and LSU to settle it once and for all at the White House. He was joking. Or was he?Story | Conversation | March 23, 2004


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Increase the livability of your outdoor space with the elements of fire, water, earth and a little shade.
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How to Turn an Old Entertainment Center into a Play Kitchen 18 Photos
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Behind the Build: An Inside Look at Vanilla Ice and His Crew 16 Photos
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text: ""; ?> Auf Der Maur guitar chords, guitar tabs and lyrics - chordie guitar tabs, guitar chords and lyrics Artists Songs Public booksPublic My songbookSongbook ResourcesDiv Forum Browse artists: A B C D E F G H I J K L M N O P Q R S T U V W X Y Z # -first letter-A B C D E F G H I J K L M N O P Q R S T U V W X Y Z # All songs - change Chordie needs editors! Improve this page Auf Der Maur MusicAuf Der Maur Songs There was no article about Auf Der Maur in Wikipedia. If you know anything about Auf Der Maur, please contribute to the free encyclopedia by writing an article. Auf Der Maur Beast Of Honor (1) Beast Of Honour (1) Devils Plaything (1) Followed The Waves (1) My Foggy Notion (1) Real A Lie (1) Taste You (1) play their songs - buy their music - support the artists copyright © 2011 chordie guitar chords 5 popular songs Everythingidoidoitforyou Brian Adams Honky Tonk Women Rolling Stones Hit The Road, Jack Traditional Let It Be Beatles Take Me Home Country Roads John Denver


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is this true ? If so why?
Depends what he means by "wipe out." Did he mean that the HOA lien would be paid off? If that's the case, he's probably right, since In Michigan, liens for homeowners association dues in the case of condos are usually superior in lien position to the first mortgage REGARDLESS of when they occurred. (So are mechanics liens or liens for any repairs or construction work performed on the property.)
It may be true. Assuming that the bank's lien is superior to the HOA lien (that is, it was filed first) and the property isn't worth enough to cover both liens, the HOA lien would be "wiped out," because the first lienholder has to be paid in full before the second gets anything.
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Shop Now & Save $200 on RIKON Deluxe Bandsaw
This 14" deluxe beauty is perfect for any shop.
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Cool Retro Green Shop Stool $49.99
Take a load off with this chrome retro stool.
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Look up product instructions, videos and articles about your favorite tools using our Learning Center.
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Product Information:
Learn to create beautiful, functional powder horns with the help of this book. 275 color photos illustrate working, shaping, decorating, and finishing techniques. Historic and modern tools and equipment are introduced for inlays, engrailing, and scrimshaw. Leather strap weaving and braiding methods are also provided for carrying the finished horn in an authentic manner.
For inspiration, a photo gallery shows finished powder horns and a contact list includes the artists featured. There is no better resource for the craftsman seeking to make an authentic, functional powder horn.
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Added Feb 14, 2013
by krystyna
Swindon, United ...
This is my entry for BurdaStyle Sewing Vintage Modern Contest 2013
Option Three: Create your own variations projects ,using one or more master pattern(s) ,and/or variations project(s) from the BurdaStyle sewing Vintage Modern Book .
I love this 50s style.
The dress is made from large check taffeta fabric ,and is fully lined with one layer netting that give the skirt extra volume.
My dress insted gathered waist got pleated skirt ,that I think works well with large check .
Neck line is finished in oval ,and upper bodice is made from fabric cut on bias .
I dress up my garment with chiffon sach ,but will look good with any matching belts in waist .
Feb 15, 2013, 11.29 PMby jaslyn117
I love this Dress! I like how you did it diamondy on top. :)
Feb 15, 2013, 07.42 PMby liliv-1
What a dress!
Feb 15, 2013, 11.02 AMby Sabrina Wharton-Brown
Very nice!
Feb 15, 2013, 09.56 AMby martinK
that is awesome!!! wonderfull
Feb 14, 2013, 09.53 PMby MojoHollz
Gorgeous, your fabric is amazing the the dress is superb!
Feb 14, 2013, 07.58 PMby lelie
What a lovely dress! I'm in love!
Feb 14, 2013, 02.19 PMby Linda74Sews
Absolutely darling! Love the check pattern of the fabric and the great modern version of a 50's look. Bravo!
Feb 14, 2013, 01.08 PMby kisses
Lovely and the fabric is nice
Feb 14, 2013, 11.45 AMby Mary Athey
Stunning dress!! Good luck x
Feb 14, 2013, 12.06 PMby krystyna
Add to Favorites 19 people added it
BurdaStyle Magazine US
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Learn how to use your bodice sloper to draft this popular collar style
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This is a quintessential ladylike coat with turned down shawl collar and fit and flare silhouette.
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Networking SpecialistBookmark This
Glamour n'Glitz Events
16484 Frederick rd
Woodbine, MD
DescriptionGlamour n'Glitz events is in search for a person who is outgoing, talkative, organized, professional, and mature.
Glamour n'glitz events LLC is in need of a networking specialist. This person is to attend shows, and networking meetings on a part time basis. You must be able to manage your time, be able to communicate on a fun, professional level.......... and have a car!
Compensation: $15 an hr when you attend shows and networking meetings. Client referrals also gets you $100.00 per contract sign! Added benefit of being able to work the event to earn more.


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Intro-Tech Diamond Plate Cargo Liner DP-CM-CV-488
- Our Price: $83.99
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Purchase this item by Tuesday, Dec 9th for Delivery by Dec 24th
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Diamond Plate Medium Cargo Mat
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- Diamond Plate
- with Bench Seat Up
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Stories from the Field
Our Stories from the Field blogs are undergoing an upgrade! They will be back for the 2014/2015 programming year. To learn more about what our youth volunteers, host families and community partners are up to in the field, join our global on-line community!
- Like us on Facebook: Canada World Youth – Jeunesse Canada Monde
- Follow us on Twitter: @cwyjcm
- Subscribe to our You Tube channel
- Are you one of our fabulous CWY Alumni? Reconnect with CWY!
CWY Initiatives
Results: Jamie Rooney & Ben Morrison Cross-Canada Fundraising Initiative
A six-month exchange that changed their world Jamie Rooney & Ben Morrison's Cross-Canada Cycling Fundraiser Founded in 1971, Canada World Youth (CWY) is a world leader in developing international educational programs for young people aged 15 to 35. A non-profit organization, CWY is dedicated to enriching the lives of young people that have a desire…
Giving Means: Community-Driven, People-Funded Development
Development Components of the Youth Leaders in Action Program
Upcoming Events
Meet with our Kenyan and Tanzanian Partners in Sudbury, ON!
SUDBURY, ON. Canada World Youth (CWY) invites you to a special gathering on November 28, 2014 at Laurentian University's School of the Environment where our partners from Kenya and Tanzania will share the impact of our work in East Africa. Organized by current youth volunteers from the women's Canada/Kenya/Tanzania project with the support of Laurentian…
CWY Presents: An East African Cultural Night
Latest News
Canada World Youth receives international partners and Alumni in Montreal
Montreal, QC. (November 24, 2014) – On November 20, Mr. Marc LePage, Treasurer, Mrs. Josée St-Pierre, member of the Board of Directors, and Ms. Rita S. Karakas, President & CEO of Canada World Youth (CWY), received international partners, and met with youth volunteers, CWY Alumni, and the public in Montreal. The event served as an…
Canada World Youth welcomes partners in Ottawa!
International CWY Partner Program Visit


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Millinocket House Rental Photos and Description
3 Bedrooms, 2 Lofts, 2 1/2 Baths
Three season lakefront vacation home with spectacular views of the sun setting over the mountains of the last hundred miles of the Appalachian Trial. One of the most unique properties in the area. This home sleeps up to12 with all the beauty Maine has to offer right out the front door. Located right on the ITS snowmobile/four wheeler trail. In the Summer there is world class white water rafting, hiking the tallest peak in Maine, or down into the grand canyon of Maine, or just relaxing on the beach out front. The Fall boasts the most beautiful leaf foliage in the country, along with moose watching as well. The lake house itself is a 2 1/2 story artist designed home that needs to be seen to be appreciated.
call for spring deals!:
- Suitability:
- Long-term Renters Welcome
- children welcome
- non smoking only
- pets considered
About Kim Chasse
Welcome to Kitty's Lake House, the home I designed and physically built myself in honor of the memory of my Mother, who was as unique as you will find this place and the breathtaking spot it rests on. I grew up in Millinocket and moved to southern Maine where I work as a high school art teacher. My vacations always brought me home to this amazing place and this gave me the incentive to build this one of a kind property.
Kim Chasse