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High Level Describable Attributes for Predicting Aesthetics and Interestingness
- Warren Beasley
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1 High Level Describable Attributes for Predicting Aesthetics and Interestingness Sagnik Dhar Vicente Ordonez Tamara L Berg Stony Brook University Stony Brook, NY 11794, USA Abstract With the rise in popularity of digital cameras, the amount of visual data available on the web is growing exponentially. Some of these pictures are extremely beautiful and aesthetically pleasing, but the vast majority are uninteresting or of low quality. This paper demonstrates a simple, yet powerful method to automatically select high aesthetic quality images from large image collections. Our aesthetic quality estimation method explicitly predicts some of the possible image cues that a human might use to evaluate an image and then uses them in a discriminative approach. These cues or high level describable image attributes fall into three broad types: 1) compositional attributes related to image layout or configuration, 2) content attributes related to the objects or scene types depicted, and 3) sky-illumination attributes related to the natural lighting conditions. We demonstrate that an aesthetics classifier trained on these describable attributes can provide a significant improvement over baseline methods for predicting human quality judgments. We also demonstrate our method for predicting the interestingness of Flickr photos, and introduce a novel problem of estimating query specific interestingness. 1. Introduction Automating general image understanding is a very difficult and far from solved problem. There are many subproblems and possible intermediate goals on the way toward a complete solution, including producing descriptions of what objects are present in an image (including their spatial arrangements and interactions), what general scene type is shown (e.g. a beach, office, street etc.), or general visual qualities of the image (such as whether a picture was captured indoors, or outside on a sunny day). While none of these are solved problems either, progress has been made in the research community toward partial solutions. In this paper we build on such progress to develop techniques for estimating high level describable attributes of im- Figure 1. High Level describable attributes automatically predicted by our system. ages that are useful for predicting perceived aesthetic quality of images. In particular we demonstrate predictors for: 1. Compositional Attributes - characteristics related to the layout of an image that indicate how closely the image follows photographic rules of composition. 2. Content Attributes - characteristics related to the presence of specific objects or categories of objects including faces, animals, and scene types. 3. Sky-Illumination Attributes - characteristics of the natural illumination present in a photograph. We use the phrase high level describable attributes to indicate that these are the kinds of characteristics that a human might use to describe an image. Describability is key here so that we can ask people to label images according to the presence or absence of an attribute and then use this labeled data to train classifiers for recognizing image attributes. Recent work on attributes for faces has shown that for face verification, describable facial attributes can produce better performance than purely low level features [12]. While our focus is on attributes of images not of faces, we pursue a similar direction to demonstrate the power of attributes for: estimation of aesthetic quality (Sec 3.1), estimation of general interestingness (Sec 3.2), and a new problem of estimation query specific interestingness (Sec 3.3). 1657
2 Figure 2. Overview of our method for estimating interestingness (aesthetic quality follows a similar path). From left to right: a) example input image b) low level features are estimated c) high level attributes are automatically predicted by describable attribute classifiers, d) interestingness is predicted given high level attribute predictions (or optionally in combination with level features [11] dashed line). While much previous work on aesthetics prediction has provided intuition about what high level attributes might be useful, they have used this intuition to guide the design of relevant low level image features. Our approach, on the other hand explicitly trains classifiers to estimate describable attributes and evaluates the accuracy of these estimates. Furthermore, we demonstrate that classifiers trained on high level attribute predictions are much more effective than those trained on purely low level features for aesthetics tasks, and can be made even more accurate when trained on a combination of low level features and high level attributes (fig 5). Our other main contributions include a focus on extracting high level visual attributes of images (as opposed to objects), and novel attributes related to image layout Previous Work Our work is related to three main areas of research: estimating visual attributes, estimating the aesthetics of photographs, and human judgments of aesthetics. Attributes: Recent work on face recognition has shown that the output of classifiers trained to recognize attributes of faces gender, race, age, etc. can improve face verification [12]. Other work has shown that learning to recognize attributes can allow recognition of unseen categories of objects from their description in terms of attributes, even with no training images of the new categories [13, 5, 7]. Our work is related to these methods, but while they focus on attributes of objects (e.g. blond person, or red car), we look at the problem of extracting high level describable attributes of images (e.g. follows rule of 3rds ). Aesthetics: There has been some previous work on estimating the aesthetic quality of images, including methods to differentiate between images captured by professional photographers versus amateurs [24, 11, 25, 3, 23, 17]. This prior work has utilized some nice intuition about how people judge aesthetic quality of photographs to design low level features that might be related to human measures. Datta et al select visual features based on artistic intuition to predict aesthetic [3] and emotional quality [4]. Tong et al use measures related to the distortion [25]. Ke et al select low level features such as average hue, or distribution of edges within an image, that may be related to high level attributes like color preferences or simplicity [11]. Our method is most similar to Luo & Tang [17], who also consider ideas of estimating the subject of photographs to predict aesthetic quality. The main difference between these approaches and ours is that instead of using human intuition to design low level features, we explicitly train and evaluate prediction of high level describable attributes. We then show that aesthetics classifiers trained on these attributes provides a significant increase in performance over a baseline method from Ke et al (fig 5). Our overall performance is comparable to the results reported in Luo & Tang [17], but seems to perform somewhat better, especially in the high precision low recall range arguably the more important scenario for users trying to select high aesthetic quality photographs from large collections. Human Judgment of Aesthetics: The existence of preferred views of objects has long been studied by Psychologists [20]. Photographers have also proposed a set of com- 1658
3 position rules for capturing photos of good aesthetic quality. In some interesting recent work there have been several studies that expand the idea of view preferences to more general notions of human aesthetics judgment, including ideas related to compositional rules. These experiments include evaluating the role of color preferences [22, 18] and spatial composition [8, 1]. Other work in computational neuroscience has looked at developing models of visual attention including ideas related to saliency e.g. [10]. Some of our attributes are directly related to these ideas, including predicting the presence of opposing colors in images, and attributes related to the presence of salient objects, and arrangement of those objects at preferred locations Overview of Approach The first phase of our work consists of producing high level image attribute predictors (Secs 2.1, 2.2, 2.3). We do this by collecting positive and negative example images for each attribute, picking an appropriate set of low level features, and training classifiers to predict the attribute. In each case labelers are presented with an image and asked to label the image according to some attribute, for example Does this image follow the rule of thirds?. Possible answers are yes, not sure, or no and only images that are consistently labeled as yes or no are used for training. Next we demonstrate that these high level attribute predictors are useful for estimating aesthetic quality (DPChallenge) and interestingness (Flickr). For each application, a set of training images is collected consisting of highly ranked images as positive examples and low ranked images as negative examples. A classifier is then trained using the output of the high level attribute predictors we developed as features and evaluated on held out data. We also show results for training classifiers using only low level features, and using a combination of low level features and our high level attributes. Results on aesthetics for DPChallenge are in Sec. 3.1 and interestingness for Flickr are in Sec Finally we show results on ranking for specific query interestingness in Sec Describable attributes We have developed high level describable attributes (examples in fig 1) to measure three types of image information: image composition (Sec 2.1), image content (Sec 2.2), and sky-illumination (Sec 2.3) Compositional Attributes Our compositional attributes address questions related to the arrangement of objects and colors in a photograph, and correspond to several well known photographic rules of composition. These compositional attributes are: Presence of a salient object a photo depicting a large salient object, well separated from the background. Low depth of field Saliency and Rule of Thirds Figure 3. Example describable attribute computations. Top Low DoF (left: original image, center: wavelet transform, right: wavelet coefficients and center surround computation). Bottom salient object presence and rule of 3rds (left: original pic, center: detected salient object region, right: centroid and conformity to rule of 3rds). Rule of Thirds a photo where the main subject is located near one of the dividing third-lines. Low Depth of Field the region of interest is in sharp focus and the background is blurred. Opposing Colors a photo that displays color pairs of opposing hues. Presence of a salient object: We predict whether an image contains some large object, well separated from its background. To do this we take advantage of recent developments in automatic top down methods for predicting locations of salient objects in images [16]. As input image descriptors we implement 3 features related to saliency: a multi-scale contrast map, a center surround histogram map, and a center weighted color spatial distribution map efficiently computing the features using integral image techniques [21]. All three of these feature maps are supplied to a conditional random field (CRF) to predict the location of salient objects (fig 3 shows a predicted saliency map). The CRF is trained on a set of images that contain highly salient objects. If an image does not contain a salient object, then the CRF output (negative of the log probability) will be high estimated by the free energy value of the CRF. We evaluate classification accuracy for this attribute using a set of 1000 images that have been manually labeled as to whether they contain a salient object. Precision-recall curves for predicting the presence of a salient object are shown in figure 4 (left plot, red), showing that our salient object predictor is quite accurate at estimating the presence of a salient object. Rule of thirds: If you consider two vertical and two horizontal lines dividing the image into 6 equal parts (blue lines fig 3), then the compositional rule of thirds suggests that it will be more aesthetically pleasing to place the main subject of the picture on one of these lines or on one of their intersections. For our rule of thirds attribute, we again make use of the salient object detector. We calculate the 1659
4 Figure 4. Left: Precision-Recall curves for some Compositional attributes: Salient Object Present, Follows Rule of 3rds, Displays Opposing Colors. Right: Precision-Recall curves for some Content attributes: presence of animals, indoor-outdoor, various scenes. minimum distance between the center of mass of the predicted saliency mask and the 4 intersections of third-lines. We also calculate the minimum distance to any of the thirdlines. We use the product of these two numbers (scaled to the range [0,1]) to predict whether an image follows the rule of thirds and evaluate this attribute on manually labeled images. Precision-recall curves are shown in fig 4 - left green. Low depth of field: An image displaying a low depth of field (DoF) is one where objects within a small range of depths in the world are captured in sharp focus, while objects at other depths are blurred (often used to emphasize an object of interest). For our low DoF attribute we train an SVM classifier on Daubechies wavelet based features, indicative of the blurring amount [3]. The wavelet transform is applied to the image and then we consider the third level coefficients of the transformation in all directions (fig 3). Using a 4x4 grid over the image, we divide the sum of the coefficients in the four center regions by the sum of coefficients over all regions, producing a vector of 3 numbers, one for each direction of the transformation. A manually labeled dataset of 2000 images from Flickr and Photo.net is used to train and test our low DoF classifier. Precisionrecall curves for the low DoF attribute are shown in fig 4 - left plot, cyan - demonstrating reliable classification. Opposing colors: Some color singles, pairs, or triples are more pleasing to the eye than others [22, 18]. This intuition gives rise to the opposing colors rule which says that images displaying contrasting colors (those from opposite sides of the color spectrum) will be aesthetically pleasing. For this attribute we train classifiers to predict opposing colors using an image representation based on the presence of color pairs. We first discretize pixel values into 7 values. We then build a 7x7 histogram based on the percentage of each color pair present in an image and train an SVM classifier on 1000 manually labeled images from Flickr. Classification accuracy is shown in fig 4 - left plot, blue. Our classifier for this attribute is not extremely strong because even images containing opposing colors may contain enough other color noise to drown out the opposing color signal. However, this attribute still provides a useful signal for our aesthetics and interestingness classifiers Content Attributes Content is often a large contributor to human aesthetic judgment. While estimating complete and accurate content is beyond current recognition systems, we present a set of high level content attributes that utilize current state of the art recognition technologies to predict: Presence of people a photo where faces are present. Portrait depiction a photo where the main subject is a single large face. Presence of animals whether the photo has animals. Indoor-Outdoor classification whether the photo was captured in an indoor setting, Scene type 15 attributes corresponding to depiction of various general scene types (e.g. city, or mountain). Presence of people & Portrait depiction: We use the Viola-Jones face detector [26] to estimate the presence of faces in an image (a proxy for presence of people). For this attribute we output a binary classification (1, if faces have been detected, and 0 otherwise). We manually label a test dataset of 2000 images from Photo.net and obtain 78.9% accuracy for predicting face presence. For portrait depiction we classify images as positive if they produce a face detection of size greater than 0.25 image size. We evaluate this feature on 5000 images from Photo.net hand labeled as portrait or non-portrait images and obtain 93.4% accuracy. Object and scene attributes: For the presence of animals, indoor-outdoor classification and scene type attributes we train 17 SVM classifiers using the intersection kernel computed on spatial pyramid histograms [14] (1 each for animals, and indoor-outdoor, and 15 for various scene categories). In particular we compute the SPM histograms on visual dictionaries of SIFT features [15] captured on a uniform grid with region size 16x16 and spacing of 8 pixels. The SIFT features for 100 random images are clustered 1660
5 Figure 5. Precision-Recall curves for aesthetics estimation (left) and interestingness estimation (right - averaged over 6 queries and general Flickr set). In both cases we show estimation using low level image features with Naive Bayes classification (ie method proposed in Ke et al [11] black), the same low level image features using SVM classification (red), our high level describable attributes with SVM classification (blue), and a combination of low level features and high level attributes with SVM classification (green). For both aesthetics and interestingness our high level attributes (blue) produce significantly powerful classifiers than the previous method (black), and can provide complimentary information when used in combination with low level features (green). to form a single visual dictionary which is used for all of the content attribute types. For each of these attributes we use an appropriate data set for training and testing. For presence of animals this is the Animals on the Web dataset [2] with images from all 10 animal categories merged into an animal superclass. For indoor-outdoor this is 2000 images collected from Flickr (half indoor, half out). For the 15 scene type attributes this is the 15 scene category dataset [19, 6]. Precision-recall curves for each attribute (subsampled for scenes for clarity of presentation) are shown in fig 4. Though it is well known that recognition of specific animal categories is very challenging [2], we do quite well at predicting whether some animal is present in an image. The indoor-outdoor classifier is very accurate for most images. For scenes, natural scene types tend to be more accurate than indoor scenes Sky-Illumination Attributes Lighting can greatly effect perception of an image e.g. interesting conditions such as indirect lighting can be more aesthetically pleasing. Because good indoor illumination is still a challenging open research problem, we focus on natural outdoor illumination through 3 attributes: Clear skies photos taken in sunny clear conditions. Cloudy skies photos taken in cloudy conditions. Sunset skies photos taken with sun low in the sky. To train our sky attribute classifiers we first extract rough sky regions from images using Hoeim et al s work on geometric context [9]. This work automatically divides image regions into sky, horizontal, and vertical geometric classes using adaboost on a variety of low level image features. On the predicted sky regions we compute 3d color histograms in HSV color space, with 10 bins per channel, and train 3 sky attribute SVMs using 1000 manually labeled images from Flickr. The classifiers produced are extremely effective (99%, 91.5% and 96.7% respectively). 3. Estimating Aesthetics & Interestingness 3.1. Aesthetics The first task we evaluate is estimating the aesthetic quality of an image. Here the goal is to differentiate between images of high photographic quality from images of (low) snapshot quality. Experiments: Because aesthetic quality is by nature subjective, we make use of human evaluated images for training and testing. We collect a dataset of 16,000 images from the DPChallenge website 1. These images have been quantitatively rated by a large set of human participants (many of whom are photographers). We label the top 10% rated photos as high aesthetic quality, and the bottom 10% as low quality to allow a direct comparison to Ke et al [11]. Going further down in the ratings is possible, but increases ambiguity in ratings. Half of each of these sets is used for training, while the remaining half is used for evaluation. To estimate aesthetic quality we train an SVM classifier where the input image representation is the outputs of our 26 high level describable attribute classifiers (fig 2 shows our pipeline). For comparison we also reimplement the baseline aesthetics classifier used in Ke et al [11]. We show results of their original Naive Bayes classification method (fig 5, left plot black) and also train an SVM on their low level features (fig 5, left plot red). Our high level attributes produce a significantly more accurate ranking than the previous approach, and when used in combination with these low level features can produce an even stronger classifier (fig 5 left plot, green). This suggests that our high level
6 Figure 6. General Flickr photos ranked by interestingness. Top 5 rows show the first 50 images ranked by our interestingness classifier. Bottom two rows show the last 20 images ranked by our interestingness classifier. attributes are providing a source of useful complimentary information to purely feature based approaches General interestingness We also apply our describable attributes to a related, but deceptively different problem of estimating interestingness of photos. While DPChallenge directly measures aesthetic quality through user ratings, Flickr s interestingness measure 2 is computed more indirectly through analysis of social interactions with that photo (viewing patterns, popularity of the content owner, favoriting behavior, etc). Experiments: For our general interestingness task we collect a dataset of 40,000 images from Flickr using interestingness-enabled Flickr searches on time limited queries. The top 10% of these images are used as positive examples for our interestingness classifier, while the bottom 10% are used as negative examples (splitting this set in half for training and testing). Again here we train an SVM classifier to predict interestingness using our 26 describable attribute classifications as input (fig 5, right plot blue). For comparison, we also train an interestingness classifier on the low level features used in Ke et al [11] using their original Naive Bayes approach (fig 5, right plot black), and using an SVM classifier 2 patent # (fig 5, right plot red). Lastly we train a combined classifier on their low level features and our high level attribute classifications, a 32 dimension input feature vector (fig 5, right plot green). In fig 6 we show images ranked by automatically predicted interestingness score. The top 5 rows show 50 highly ranked images, and the bottom 2 rows show 20 low ranked images and reflect the variation in interestingness between the top and bottom of our ranking. In fact, our method performs extremely well at estimating interestingness (fig 5 right plot). The high level attributes produce a powerful classifier for predicting interestingness (fig 5 blue), and improve somewhat with the addition of low level features (fig 5 green). Compared to aesthetics classification, our interestingness classifier shows an even larger increase in performance over the previous method (fig 5, black vs blue) Query specific interestingness Lastly, we introduce a method to produce query specific interestingness classifiers. In general we expect some of our attributes to be more useful for predicting interestingness than others. We also expect that the usefulness of an attribute might vary according to the specific search query used to collect images e.g. low DoF may be more useful for predicting interestingness of images returned for the 1662
7 beaches buildings cars horses insects person error (smaller is better) Error for general vs class specific interestingness classifiers General interestingness classifier Class specific interestingness classifiers Figure 7. Query specific error rates for interestingness prediction. For some categories, the query specific classifiers (blue) has significantly lower error than the general interestingness classifier (red). query insect than for the query beach. Experiments: We collect a dataset of images from Flickr using 6 different query terms: beach, building, car, horse, insect, and person, retrieving 20,000 images for each query ranked by interestingness. Again the top 10% are labeled as positive, bottom 10% as negative and the collection is split in half for training and testing. For each query collection we train an interestingness predictor. We then evaluate the accuracy of our general interestingness classifier vs using our query specific classifiers to rank images from the held out test set. For some queries, the query specific classifiers outperform the general interestingness classifier (error rates are shown in fig 7). Ranked results for some of our query specific interestingness classifiers are shown in fig 8 where top 3 rows show 30 most highly ranked images and bottom rows show the 10 lowest ranked images for each query. At the top of the beach ranking we observe very beautiful, clear depictions, often with pleasant sky illuminations. At the bottom of the ranking we see more cluttered images often displaying groups of people. For the insect query, the top of the ranking shows images where the insect is the main subject of the photograph, and a low DoF is often utilized for emphasis. In general for each category the top of our ranking shows more picturesque depictions while the bottom shows less clean or attractive depictions. Acknowledgments This work was supported in part by NSF Faculty Early Career Development (CAREER) Award # References [1] O. Axelsson. Towards a psychology of photography: dimensions underlying aesthetic appeal of photographs. In Perceptual and Motor Skills, [2] T. L. Berg and D. A. Forsyth. Animals on the web. In CVPR, [3] R. Datta, D. Joshi, J. Li, and J. Z. Wang. Studying aesthetics in photographic images using a computational approach. In ECCV, , 1660 [4] R. Datta, J. Li, and J. Z. Wang. Algorithmic inferencing of aesthetics and emotion in natural images: An exposition. In ICIP, [5] A. Farhadi, I. Endres, D. Hoiem, and D. A. Forsyth. Describing objects by their attributes. In CVPR, [6] L. Fei-Fei and P. Perona. A bayesian hierarchical model for learning natural scene categories. In CVPR, [7] V. Ferrari and A. Zisserman. Learning visual attributes. NIPS, [8] J. Gardner, C. Nothelfer, and S. Palmer. Exploring aesthetic principles of spatial composition through stock photography. In VSS, [9] D. Hoiem, A. A. Efros, and M. Hebert. Geometric context from a single image. In ICCV, pages , [10] L. Itti and C. Koch. Computational modelling of visual attention. Nature Reviews Neuroscience, 2(3): , Mar [11] Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In CVPR, , 1661, 1662 [12] N. Kumar, A. Berg, P. Belhumeur, and S. Nayar. Attribute and simile classifiers for face verification. In ICCV, , 1658 [13] C. Lampert, H. Nickisch, and S. Harmeling. Learning to detect unseen object classes by between-class attribute transfer. In CVPR, [14] S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching. In CVPR, June [15] D. G. Lowe. Distinctive image features from scale invariant keypoints. IJCV, [16] T. Lui, J. Sun, N.-K. Zheng, X. Tang, and H.-Y. Shum. Learning to detect a salient object. In CVPR, [17] Y. Luo and X. Tang. Photo and video quality evaluation: Focusing on the subject. In ECCV, [18] C. Nothelfer, K. B. Schloss, and S. E. Palmer. The role of spatial composition in preference for color pairs. In VSS, , 1660 [19] A. Oliva and A. Torralba. Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV, [20] S. Palmer, E. Rosch, and P. Chase. Canonical perspective and the perception of objects. In Attention and Performance, [21] F. Porikli. Integral histogram: A fast way to extract histograms in cartesian spaces. In CVPR, [22] K. B. Schloss and S. E. Palmer. Color preferences. In VSS, , 1660 [23] X. Sun, H. Yao, R. Ji, and S. Liu. Photo assessment based on computational visual attention model. In ACM MM, [24] H. Tong, M. Li, H. Zhang, J. He, and C. Zhang. Classification of digital photos taken by photographers or home users. In PCM, [25] H. Tong, M. Li, H. Zhang, C. Zhang, J. He, and W.-Y. Ma. Learning no-reference quality metric by examples. In ICMM, [26] P. Viola and M. Jones. Robust real-time object detection. In IJCV,
8 Figure 8. Query specific interestingness ranking for search terms (beach, insect, car). Top three rows for each query show the most highly ranked images. Bottom rows for each query show the least highly ranked images. 1664
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Mr. Chris Page
Sydney - Australia
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About Mr. Chris Page
After 10 years in Insurance and Media sales Chris joined the Recruitment Industry in 2001 and has progressively moved through the ranks.

Initially Chris joined a boutique IT recruitment agency focusing on contract and permanent placements. An interest in the general staffing industry saw Chris Join Global Solutions in 2003 where he learnt the ropes in the position of researcher. The next 2 years saw him work in a number of roles including research manager and managing consultant. Chris has been responsible for conducting searches in the IT / banking / construction and finance markets. In 2005 Chris was promoted to help develop the operational side of our international placement business.

Today Chris spreads his time between our International offices providing support to the country managers on an operational level, conducting campaigns on a global basis to source talent as well as managing our International placement business. Chris has become an expert in visa law, tax, cost of living and perhaps most importantly the cultural elements of each country and region. Chris travels in excess of 100,000 miles a year in his quest to further his knowledge of these various elements all designed to facilitate informed and intelligent advice. Lastly Chris is responsible for co-ordinating with our various business units ensuring a candidates application is dealt with efficiently and followed through effectively.

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text:
Thank you for your question. Your are a great candidate for a filler. You would only need 0.5-1cc of filler. Consult with a board certified plastic surgeon. Best Wishes
I am considering getting a lip filler. How much Juvederm would be recommended to give me plumper lips? (photos)
Doctor Answers 12
I am considering getting a lip filler. How much Juvederm would be recommended to give me plumper lips
Juvederm lip filler
Thank you for posting your question. In believe starting out of conservative is best in the case of filler in the lips, so I would start with 1/2cc or 1cc. You can always reassess and add more at a later time. You have a pretty shape to your lips naturally, so I think a small amount of volume would compliment you. Hope this helps!
Best wishes,
Dr. Michael J. Brown
Northern Virginia Plastic Surgeon
Learn more: https://www.loudouncenterforplasticsurgery.com/
Thank you for your question and photo. You have a nice shape to your lips. I always tell my patients to start off with 1 syringe. You will have swelling after the injections so we typically tell our patients to wait at least 2 weeks before you decide if you want to add more filler. Its always best to start conservative and add more rather then getting to much product all at once. Its best to consult with a board certified plastic surgeon.
Best of luck to you!
You might also like...
It is best to start out with a small amount (1/2-1 syringe) and look in the mirror, then add additional filler if desired. To be sure what is best for you, see two or more board-certified providers in your area for a complete evaluation to make sure you are a good candidate and that it is safe for you to have treatment.
I hope this helps.
Learn more: http://www.drjjwendel.com/fillers
I am considering getting a lip filler. How much Juvederm would be recommended to give me plumper lips? (photos)
Keep in mind you can always add more. If you are over injected then something can be used to remove the product but it gets rid of all of it. I would start with a single syringe and see how that looks. After 7-10 days if you'd like more then another syringe could be opened. I'd also recommend you be conservative to avoid having an overdone look since you already have a nice shape to your lip.
I would recommend you consult with a facial plastic surgeon, plastic surgeon, or dermatologist for an in person evaluation to determine what is the best treatment.
I hope this helps and good luck.
Learn more: http://www.DrGuyFacialPlastics.com/Fillers
Lip augmentation
Since you have some lip volume, I would start with 1cc for both lips. And then have you come back in a few weeks to see if we needed more once the swelling goes down
The best way to assess and give true advice would be an in-person exam.
Please see a board-certified plastic surgeon that specializes in aesthetic and restorative plastic surgery.
best of luck!
Dr Schwartz
Best Natural Lip Filler Injections
You have nice shaped lips. I would use a small amount of filler to just re-inflate them to their naturally full state. We would use between 0.5-1.0 cc of Juvederm Plus XC with a micro-canulla. Make sure to choose your treating physician most carefully. Good luck and be well.
Dr. P
Filler To Lips , How Much Is Too Much?
Learn more: https://www.youtube.com/watch?v=LymEuAVm56k
Adding Filler To Already Full Lips
Learn more: http://www.zcosmetichealth.com/juvederm/get-sexy-plump-lips-always-wanted-injectable-fillers/
Lip filler
These answers are for educational purposes and should not be relied upon as a substitute for medical advice you may receive from your physician. If you have a medical emergency, please call 911. These answers do not constitute or initiate a patient/doctor relationship.


text:
Let us know when your receive great advice so we can thank our lawyers and point others with similar questions in the right direction.Got it!
There's this girl that is going out of her way to ruin my personal life. She is spreading lies about me and telling my girlfriend that i am seeing other girls. this is not the first time this happened, she is making it her personal mission to destroy my intimate life.
If you can prove that she said this, that it was a lie, you can sue for slander. Unless she has money, you get satisfaction. You might be able to bring a harassment injunction / restraining order case against her.
This is properly a personal injury type of case, not criminal defense. I will change the heading so that it will come to the attention of those lawyers.
If any answer on AVVO helps you, mine or someone else's, please mark it as "helpful" or "best answer" to help AVVO know which answers to show others.
Thank you.
--- Experienced Criminal Defense Lawyer in Madison, Wisconsin
--- Facebook Page
--- Talking to the Police - Advice from Lawyers and Police
--- Miranda Rights (and Wrongs)
Confidential information should not be disclosed in this Internet forum. I am a Wisconsin lawyer. The laws in each jurisdiction can be very different. I cannot give legal advice over the internet nor can I establish an attorney client relationship with you. You should NOT assume or otherwise conclude that there is an attorney -client relationship between any reader and this writer or his firm. These comments are only guideposts. They are not subject to any privilege protections. Indeed, these internet communications are neither privileged nor confidential. Accordingly, those using this form of communication need to be guarded in what they write. Because of the nature of these communications the information is general only and should not be relied upon in any specific case. This internet site is public forum, where the communications are not confidential or privileged. There may very well be merit to your defense or position in this type of situation. However, there are hardly sufficient details for an attorney to provide you with some path to follow. It is imperative that ALL of the facts in a particular situation be examined. No conclusion can be drawn from the communication that you have provided. There are some matters that are just better handled by an attorney familiar with the procedures of the courts in your area. Most, if not all, legal matters should not be handled via internet communication. At best, the responders on this site can give you a few hints and guidance. To deal with a legal problem, nothing is better than to consult with a lawyer who will give you some time and advice. If you cannot afford an attorney, there should be agencies in your area that can provide discounted, or even free, legal services. Experienced Criminal Defense Lawyer in Madison, Dane County, Wisconsin http://addbalance.com Talking to the Police - Advice from Lawyers and Police: http://addbalance.com/police.htmAsk a similar question
Sure there is legal action -- you can sue for slander assuming she is lying. The probem is, to what end? For money? For the satisfaction of jamming her up? You would have a difficult time finding an attorney to represent you in such an action because there is most likely no money to recover and it is unlikely a jury would give you much money.
A protective order can be sought, but there are certin things you have to prove and "spreading rumors" is not usually contemplated in the protective order setting.
Good luck.
In no way am I offering you legal advice, and in no way has my comment created an attorney-client relationship. You are not to rely upon my note above in any way, but insted need to sit down with counsel and share all relevant facts before receiving fully-informed legal advice. If you want to be completely sure of your rights, you must sit down with an experienced criminal defense attorney to be fully aware of your rights.Ask a similar question
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What is JotForm?
JotForm is a free online form builder which helps you create online forms without writing a single line of code. No sign-up required.
At JotForm, we want to make sure that you're getting the online form builder help that you need. Our friendly customer support team is available 24/7.
We believe that if one user has a question, there could be more users who may have the same question. This is why many of our support forum threads are public and available to be searched and viewed. If you'd like help immediately, feel free to search for a similar question, or submit your question or concern.
Forms not being transmitted/receivedAsked by brandysilk on October 22, 2015 at 04:31 PM
URGENT: Customer has sent form and uploaded attachment but it is not being transmitted. We also tested the form and find they are not being transmitted....we are not receiving the email in the inbox and it is not in spam or junk either. The recipient email address is email@example.com. The forms have worked in the past not sure what the problem is. PLEASE ADVISE
Thank you,
@stevedup, in order to avoid confusion with our support to Amy and for us to support you better, your question has been moved to a new support thread:
We will be supporting you there.
@brandysilk, I have checked your provided email address, Amy, and it looks like it ended up in our bounce list. When emails are bouncing, it means that the recipient email server refused to accept our emails. The error message that our servers received when attempting to deliver emails is short and simple, as follows:
It looks like your recipient server was flagging our emails as spam, and had stopped accepting them soon after. As our emails were being rejected, our system placed the recipient address into the bounce list.
You email address firstname.lastname@example.com is now removed from our bounce list, but please make sure to go over your email notification settings and make sure to set them up according to this guide: How to setup email alerts to prevent email bouncing related issues.
It may also help to whitelist our servers on your end, and to add our sender email addresses firstname.lastname@example.com and firstname.lastname@example.com to your contacts list.
You can also check any of your submissions directly on JotForm, simply by opening the submissions page of your desired form. I hope this helps, Amy, please do let us know how it goes.
Thank you for the prompt reply and detailed information.
The forms are now working and I will review your setup information and do my part. Already added sender email addresses to the contacts list.
You are kindly welcome, Amy. I'm glad to hear that it helped.
If you need assistance with anything else, or if you encounter a similar issue ever again, please do not hesitate to open a new support thread about it and we will do our best to help.


text: ) Q: When I dropped a strip of bait down to about 400 feet in the Arabian Sea, some 15 miles out of Mukalla Harbor in the Republic of Yemen, I caught this strikingly patterned grouper. Alyssa spotted it and moved the rifle and sticks to get into position, but the approaching animal spotted her movement and quickly left. �That moment would be about as crucial a part of the whole game as you can get" says Capt. Brad Philipps, who has more experience than most with hook-set opportunities, as a skipper much of each year in sailfish-rich Guatemala as well as fishing marlin off Africa. Louis Vuitton Sydney Store. This is rough country that suits the predator hunter who is partial to getting out and stretching his or her legs � those determined individuals who want to experience coyote hunting at its very best. Two story system is perfect for organization of your small gear in the bottom Tink�s Coyote Mist Predator Lure is ideal for bringing in shy coyotes. I told him that we were going out to shoot at Trag Hatcher's place, and he was able to give us directions to the ranch. Louis Vuitton Sydney Store.


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WASHINGTON (AP) Julianna Smoot, who was chief of staff to U.S. Trade Representative Ron Kirk, takes over at the White House for Social Secretary Desiree Rogers. Smoot was finance director for Barack Obama's presidential campaign.
The White House said on Friday that Rogers, a longtime Chicago friend of the Obamas, was stepping down. That announcement came three months after an uninvited couple crashed the Obama administration's first state dinner. Rogers was criticized for her role in allowing the embarrassing episode to happen.
The president says Smoot shares the Obamas' commitment "to creating an inclusive, dynamic and culturally vibrant White House."
President Obama and first lady Michelle Obama issued a statement Friday thanking their longtime friend from their days together in Chicago for the "terrific job she's done" organizing more than 330 events in little more than a year in the post.
They indicated no reason for the departure, effective sometime next month after a transition period.
White House press secretary Robert Gibbs said a short time later that Rogers was neither forced out nor asked to leave. He also said he didn't think the dustup over the state dinner factored into her decision.
"She's decided it's time to go back to doing other things that she loves," Gibbs said.
Rogers' handling of the Nov. 24 state dinner came under fire after a celebrity-seeking northern Virginia couple got into the exclusive South Lawn affair without a formal invitation, despite heavy White House security. As social secretary, Rogers was in charge of the event.
It was a huge embarrassment for the White House that Tareq and Michaele Salahi got into the dinner uninvited, let alone close enough to top officials to shake hands with Obama in the receiving line and take photos with Vice President Biden, which they posted on Facebook.
Rogers later acknowledged not having staff from her office at security checkpoints to help identify guests, a departure from the practice in previous administrations. Lawmakers had demanded that she testify about her handling of the event, and one wanted to subpoena her. The White House would not allow her to testify, citing the constitutional separation of powers.
Secret Service Director Mark Sullivan told Congress that normal security protocols were not followed, and three officers were put on administrative leave following the breach in what normally is tight White House security.
Obama said weeks later that he was unhappy with everyone involved in what he described as a "screw-up" and said it would not happen again. The first lady also was said to be angry about the incident. The White House reviewed its policies and decided to station staff at its security checkpoints to help clear up discrepancies about guests.
Tall and glamorous, Rogers also was criticized for having a profile higher than the social secretaries before her. She gave interviews, appeared in glossy magazine photo spreads and dressed in high-end designer labels. She also had a front-row seat next to Vogue editor-in-chief Anna Wintour during New York Fashion Week last year.
Rogers, 50, told the Chicago Sun-Times on Friday that she was leaving because she had achieved a major goal of the Obamas: turning the White House into the "people's house" by opening it up to many of those who normally do not get to visit.
"My work was really to create this framework. I think I completed that work," she told her hometown newspaper. "Our office has been able to lay the foundation for what will be known as the 'people's house' and it has already taken shape."
Rogers said she planned to explore opportunities in the corporate world, where she worked before joining the administration. She arrived in Chicago after getting an MBA and has worked at AT&T and a gas and utilities company.
Gibbs said she personally informed the Obamas of her decision around the beginning of the year.
"When she took this position, we asked Desiree to help make sure that the White House truly is the people's house and she did that by welcoming scores of everyday Americans through its doors, from wounded warriors to local schoolchildren to NASCAR drivers," the Obamas said.
Copyright 2010 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.
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Snuggle Exhilarations adds so much softness to your laundry, especially towels. The difference is unbelieveable! My laundry is no longer stiff and harsh when I take it out of the dryer.
Each time I wash my sheets in Snuggle Exhilarations, it adds so much freshness to them! There is nothing like crawling under fresh sheets at the end of a long day that smell amazing!
Each dryer causes static cling. Snuggle Exhilarations reduces it, so your clothes are going to be filled with much less static.
Go check out all of the awesome products Snuggle has to offer on their site here! You can also Follow them on Facebook and twitter!
Disclosure: The author of this blog received compensation in the form of free product in exchange for a fair and unbiased review. All opinions are 100% that of the author's.


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- Latest
- Popular
- Blogs
- Authors
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- Our Blog
… — something we see as an important project. Menlo Park City Manager Alex McIntyre weighed in on Facebook's plans with Silicon Valley Business Journal, saying: I tell people Facebook's here to stay, and they're going to become something very different from what they were in their infancy. I think the notion of the insular campus — they do have…
The social networking company says it has no immediate plans for Prologis Inc. business park, but possibilities are wide open. If market sources are correct, the $400 million pricetag would be the company's biggest real estate play.


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There's clearly no limit to what Master P's estranged wife will do to get what she's owed in their divorce.
Sonya Miller is reportedly suing her own son, rapper/actor Romeo Miller, in an attempt to expose fortunes she believes Master P is hiding. It's her belief that her hip-hop husband is using Romeo to hide his assets and make it look like he has a lot less money in his bank account.
From TMZ:
"Sonya gives examples, the main one being their record label No Limit Records, which she and P created in the '90s. She says in 2010 P and Romeo started a label called No Limit Forever, which is essentially the same company with one big difference ... Romeo is the named founder and owner, which means she can't touch it. Sonya believes it's just a ruse and controlled by Master P.
Sonya also makes mention of the reality show on Reelz called 'Master P's Family Empire,' and she says he got $750k for the first season and she believes P shielded the money by putting Romeo on the payroll docs."
In the lawsuit, Sonya refrains from blaming her son, as she believes he is just a pawn in Master P's game.
The couple, who married in 1989, have been going through divorce proceedings since October 2013. She is seeking spousal support and full custody of their children.
(Photo: Frederick M. Brown/Getty Images)


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Fine Dining at the
Restaurant at Convict Lake!
The Restaurant at Convict Lake is open year round for evening dining from 5:30pm to close 7 nights per week with the below exceptions. Reservations are recommended. To reserve your table, call 760-934-3803 or use the Opentable form to the right. If you are not up for a four course meal, enjoy appetizers, an award-winning wine list and drinks at the martini bar in The Lounge. The lounge offers a separate menu in the summer and HD TV's to watch your favorite sports.
Our menu features eclectic continental cuisine and changes a few times per year. Below please find a few menu highlights or click here to view the complete dinner menu.
| The Restaurant at Convict Reservations
Menu Highlights
Sample menu. Subject to change.
Starters :
• Clay Pot Roasted Garlic
extra virgin olive oil, balsamic syrup and soft grilled crostini
• Trio of Mushrooms
apricot coulis, fresh and dried fruit, toasted almonds, table crackers
• Burgundy Escargotserved en croûte with herbs de provence-garlic butter
| Salads:
• Baby Field Greens & Applewood Smoked Bacon
pickled cucumber-red cabbage relish, clover honey-apple cider vinaigrette
• Warm Goat Cheese & Arugula
sherry glazed shallots, tomato water,verjus and white truffle oil
• Convict Caesar
romaine hearts and herbed croutons. half order avaible
| Mains:
• Long Island Duck
seared crispy skin breast, leg confit, with a Grand Marnier, orange zest and blackberry jus lié
• Crispy Skin Scottish Salmon
crispy fingerling potatoes, sautéed spinach, finished with a fire roasted tomato vinaigrette.
• Rotolo of Fresh Pasta
ricotta cheese, wild mushroom, baby spinach finished with a tangy spicy tomato sauce and fried capers
• Beef Wellington
filet mignon and wild mushroom duxelle in puffed pastry with a bourbon glace de veau
• New Zealand Rack of Lamb
marinated with heirloom garlic, fresh herbs, grilled and accompanied by a cilantro mint sauce
| Desserts:
• Fuji Apple & Mascarpone Strudel
ala mode, with cognac butterscotch
• Vanilla Bean Crème Brûlée
• Bananas Foster
flambéed tableside


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Acting, Modeling, Dancing, Voice-Over
Hi my name is Aniya I really do enjoy acting I like to think the camera Is on me all the time and I'm tv show or show or movie
Also into modeling and fashion I have danced with my church I have been in church choir and also took acting classes at my school
Disclaimer: Casting360 is not a talent agency, employer or a talent scout; the site is only a venue. Casting360 does not promise or facilitate employment. The number of casting calls available varies by location, roles available and the level of experience required. As with any business, results may vary, and will be based on individual capacity, experience, expertise, and level of desire. There are no guarantees concerning the level of income the user may experience.
US Mailing address Casting Dynamics, 182 Howard Street #412, San Francisco, CA 94105
UK Mailing address Casting Dynamics, Ltd. ,14 Windmill Cl, Staining, FY3 0EB
© 2017 Casting360.com
Casting Dynamics, LLC \ Casting Dynamics, Ltd..


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10 reviews for Barnardo's Marylebone Shop
The Barnardo's Shop in Marylebone is no ordinary charity shop. Many of the donations are high end due to the extremely wealthy local residents.
Eduardo and the rest of the team are fun to work alongside providing the opportunity to work on the till, sorting through donations or preparing stock.
This is an excellent volunteering opportunity for anyone who enjoys working in retail / fashion and wants to provide support to a wonderful children's charity.
Great experience for a charity which really needs the help due to lack of payrolled staff and high volume of customers/stock and high standards set for stock rotation in the store
I have never seen a charity shop like it! The lay out / items on sale were amazing, and this was reflected in the customers, some of which I was told came in every day.
Had such a great time making sure the shop was presentable to the people coming in. I enjoyed helping out and learned the difficulties of running a charity shop. It's not easy so they are always in need of volunteers. The clothes and products sold are very high-end, as such the shop gets very busy.
My day in the Barnado's shop was a great experience! Eduardo runs the shop with brilliant creativty and passion, and raises some seriously impressive sums for the charity. I put together a window display, served customers and helped sort and prepare new donations. It's a great shop, with a real buzz, which is making a great contribution to Barnado's.
Barnardo's shop at Marylebone is under staffed with only one person manning the shop floor and the warehouse. Therefore it probably needs 2 volunteers each day. It was good helping out Barnardo's as we met our daily sales target and felt quite satisfying.
I really enjoyed my day at Barnado's Marylebone. Tessa, the deputy manager, was very welcoming and keen to get me involved in a range of activities. Within an hour, I was on the tills, talking to customers, and dressing mannequins for the window display. It was a really inspiring experience and I have already recommended it to my colleagues.
It was a great day! The store manager is very friendly and took me through a training session at the beginning. It was a busy day for the charity shop since it is pre-Christmas time and I really felt that I helped the charity and the customers throughout a day and made a difference. Thanks a lot.
I had a fantastic time working with Eduardo and the team at Barnardo's Marylebone.
I hope they enjoyed having me around as much as I liked being there!!
We had an absolute blast working at Barnardo's in Marylebone! What a super dedicated bunch of people and very interesting to see how a high end charity shop differs to a standard shop on the high street. Highly recommend a day with Eduardo the store manager who is an absolute star and dedicated to the cause with such passion!


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This wiki has been moved and updated recently. If you encounter any issue, please let us know by mail to firstname.lastname@example.com.
tagline: 提供: openSUSE
Welcome to the Wiki Portal edit
openSUSE Wiki は、openSUSE プロジェクトとディストリビューションについての情報源です。
Wiki の目的は、プロジェクトに参加する全ての人に対して高品質なドキュメントとコラボレーション (協同作業) の場を提供することにあります。そのためには、良く考えられた構成で、標準化され、簡単に読めるようになっていなければなりません。コンテンツは、コミュニティメンバー全員の手で作成、編集、推敲されています。
Concept edit
The content in this wiki is separated by topic in a couple of groups called namespaces. Most prominently:
- The Main namespace (with no prefix) for the presentation of the latest openSUSE Distribution, think of it as the product brochure, for people who are new to openSUSE and maybe to Linux in general.
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With this structure of the content we ensure that this wiki is only used for what it's good for.
Navigation happens through means common to any wiki, but there are also portals. Portals are entry points for a specific topic, similar to the main page. They provide an overview over a topic and guide readers to the content they seek which is either another portal or an individual article. Also categorization is very important for our wiki navigation, making it possible to automatically generate overview pages and navigational structures. With these rules for navigation we ensure that our readers find the content we produce.
Styling of content happens through templates. There are two kinds, templates for a specific kind of article like the general article template, the one for Portals or for support database articles and templates for styling of recurring content in articles like introduction and info boxes, hints and instructions or external sources and items. With these rules about styling we ensure that people understand the content we produce.
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Prominent namespaces in this wiki are subject to a quality assurance (QA) process to ensure articles meet the required quality. This QA process happens via a system of page validation which provides the opportunity to have several revisions of articles in parallel and one approved by the Wiki team. It does not limit creation of new content, but allows only quality content to be shown by default. With this process we ensure that first time visitors get drawn into page and stay.
Important part of our concept is that wiki content must be accessible to people that can't use English to read or contribute to the articles. We have multiple wiki translation projects running and we will add more when openSUSE communities using different languages are strong enough to perform the translation and later continue with a regular wiki maintenance.
We also have a multilingual wiki that can be used to start initial translation of mandatory articles in order to create translated wikis, or to translate only few articles.
Topics edit
Adding Content - If you want to contribute more than the usual typo-fix or small update for an existing article the following pages will help you to understand how we work in this wiki.
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In the news edit
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Peanut butter, chocolate, and cheesecake – a combo made in heaven! In the heat of the summer – I hate turning on the oven. This cheesecake is a tasty dessert you can enjoy without turning on an oven! I made this for the Cool Whip Challenge we had between the six of us sisters on a local TV show, Studio 5 a few weeks ago. It was a huge hit with everyone, and I'm sure your family will love it too!
Serves: 9
A tasty peanut butter, cheesecake, and chocolate dessert - no oven required!
15 minPrep Time
15 minTotal Time
- 18 regular oreos, finely crushed (in a food processor or with a rolling pin)
- 2 tablespoons butter, melted
- 1 8-oz package cream cheese, softened
- 1/3 cup sugar
- 1 8-oz container cool whip
- 1/4 cup peanut butter
- 1/4 cup powdered sugar
- 1/4 cup milk
- Mix together oreos and butter. Press firmly into the bottom of an 8x8" pan.
- In medium mixing bowl, beat cream cheese and sugar until smooth. Fold in cool whip.
- Spoon mixture into the 8x8 pan.
- In a separate bowl, mix together peanut butter, powdered sugar, and milk.
- Place mixture in dollops on top of the cream cheese, and swirl with a knife. If you want to set it up so it cuts a little better, you can refrigerate it for 2-3 hours.
Looking for more dessert recipes?
Easy Ice Cream Cake
Peanut Butter Chocolate Chip Cookie Bars
Reese's Peanut Butter Poke Cake
Dutch Apple Pie
Hawaiian Pineapple Pudding Cake


text:
Gord is a villain in Mr. Meaty. He was the owner of Soy What and hired Parker to be his new employee. Gord then revealed one day, that he and his fellow vegans were planning to eat Parker making them cannibals and read the circle of life chart and just as they were about to kill Parker, Josh rushed to save Parker and a battle began between the vegans and Josh and Parker (with no help from the cowardly Gord). When te Mr. Meaty guys won, Parker rubbed a sausage/boomerang in Gord's face and told him not to mess with the meat boys. Gord lost the respect of his employees and they went to work for Mr. Meaty.
In the episode Protest, Gord planned revenge on Josh and Parker by protesting against Mr. Meaty and it was once again up to Josh and Parker to stop him.


text:
A surprised Anu was informed by his daughter Anmol that they wanted him to come to the building's terrace. When he reached there he saw the giggling girls shouting his name.
These kids were from a Chandigarh school, who were on an outing to Mumbai. They apparently got excited when they learnt that Malik stayed opposite the hotel. Anu couldn't stop smiling and promptly recited a poem for them.


text:
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