Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain. In this paper, we propose a new method that achieves this goal with only image-level labels of whether the objects are present or not. Our approach combines a discriminative submodular cover problem for automatically discovering a set of positive object windows with a smoothed latent SVM formulation. The latter allows us to leverage efficient quasi-Newton optimization techniques. Our experiments demonstrate that the proposed approach provides a 50 relative improvement in mean average precision over the current state-of-the-art on PASCAL VOC 2007 detection.
@cite_30: We propose a method to learn heterogeneous models of object classes for visual recognition. The training images contain a preponderance of clutter and learning is unsupervised. Our models represent objects as probabilistic constellations of rigid parts (features). The variability within a class is represented by a join probability density function on the shape of the constellation and the appearance of the parts. Our method automatically identifies distinctive features in the training set. The set of model parameters is then learned using expectation maximization. When trained on different, unlabeled and unsegmented views of a class of objects, each component of the mixture model can adapt to represent a subset of the views. Similarly, different component models can also "specialize" on sub-classes of an object class. Experiments on images of human heads, leaves from different species of trees, and motor-cars demonstrate that the method works well over a wide variety of objects.
@cite_38: In this paper, we study the problem of learning from weakly labeled data, where labels of the training examples are incomplete. This includes, for example, (i) semi-supervised learning where labels are partially known; (ii) multi-instance learning where labels are implicitly known; and (iii) clustering where labels are completely unknown. Unlike supervised learning, learning with weak labels involves a difficult Mixed-Integer Programming (MIP) problem. Therefore, it can suffer from poor scalability and may also get stuck in local minimum. In this paper, we focus on SVMs and propose the WELLSVM via a novel label generation strategy. This leads to a convex relaxation of the original MIP, which is at least as tight as existing convex Semi-Definite Programming (SDP) relaxations. Moreover, the WELLSVM can be solved via a sequence of SVM subproblems that are much more scalable than previous convex SDP relaxations. Experiments on three weakly labeled learning tasks, namely, (i) semi-supervised learning; (ii) multi-instance learning for locating regions of interest in content-based information retrieval; and (iii) clustering, clearly demonstrate improved performance, and WELLSVM is also readily applicable on large data sets.
@cite_37: In this paper we investigate a new method of learning part-based models for visual object recognition, from training data that only provides information about class membership (and not object location or configuration). This method learns both a model of local part appearance and a model of the spatial relations between those parts. In contrast, other work using such a weakly supervised learning paradigm has not considered the problem of simultaneously learning appearance and spatial models. Some of these methods use a “bag” model where only part appearance is considered whereas other methods learn spatial models but only given the output of a particular feature detector. Previous techniques for learning both part appearance and spatial relations have instead used a highly supervised learning process that provides substantial information about object part location. We show that our weakly supervised technique produces better results than these previous highly supervised methods. Moreover, we investigate the degree to which both richer spatial models and richer appearance models are helpful in improving recognition performance. Our results show that while both spatial and appearance information can be useful, the effect on performance depends substantially on the particular object class and on the difficulty of the test dataset.
@cite_22: We present a method to learn object class models from unlabeled and unsegmented cluttered scenes for the purpose of visual object recognition. We focus on a particular type of model where objects are represented as flexible constellations of rigid parts (features). The variability within a class is represented by a joint probability density function (pdf) on the shape of the constellation and the output of part detectors. In a first stage, the method automatically identifies distinctive parts in the training set by applying a clustering algorithm to patterns selected by an interest operator. It then learns the statistical shape model using expectation maximization. The method achieves very good classification results on human faces and rear views of cars.
@cite_33: 
@cite_28: 
@cite_32: We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning is translation and scale-invariant; does not require alignment or correspondence between the training images, and is robust to clutter and occlusion. Category models are probabilistic constellations of parts, and their parameters are estimated by maximizing the likelihood of the training data. The appearance of the parts, as well as their mutual position, relative scale and probability of detection are explicitly described in the model. Recognition takes place in two stages. First, a feature-finder identifies promising locations for the model"s parts. Second, the category model is used to compare the likelihood that the observed features are generated by the category model, or are generated by background clutter. The flexible nature of the model is demonstrated by results over six diverse object categories including geometrically constrained categories (e.g. faces, cars) and flexible objects (such as animals).
@cite_34: We introduce an exemplar model that can learn and generate a region of interest around class instances in a training set, given only a set of images containing the visual class. The model is scale and translation invariant. In the training phase, image regions that optimize an objective function are automatically located in the training images, without requiring any user annotation such as bounding boxes. The objective function measures visual similarity between training image pairs, using the spatial distribution of both appearance patches and edges. The optimization is initialized using discriminative features. The model enables the detection (localization) of multiple instances of the object class in test images, and can be used as a precursor to training other visual models that require bounding box annotation. The detection performance of the model is assessed on the PASCAL Visual Object Classes Challenge 2006 test set. For a number of object classes the performance far exceeds the current state of the art of fully supervised methods.
@cite_20: Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learning object classifiers from weakly labeled image data, where only the presence of an object in an image is known, but not its location. Some recent work has explored the application of MIL algorithms to the tasks of image categorization and natural scene classification. In this paper we extend these ideas in a framework that uses MIL to recognize and localizeobjects in images. To achieve this we employ state of the art image descriptors and multiple stable segmentations. These components, combined with a powerful MIL algorithm, form our object recognition system called MILSS. We show highly competitive object categorization results on the Caltech dataset. To evaluate the performance of our algorithm further, we introduce the challenging Landmarks-18 dataset, a collection of photographs of famous landmarks from around the world. The results on this new dataset show the great potential of our proposed algorithm.
Although there have been recent works on convex relaxations @cite_38 @cite_28 , most MIL algorithms start from an initialization and then perform some form of local optimization. Early efforts, such as @cite_30 @cite_22 @cite_20 @cite_32 @cite_37 @cite_34 @cite_33 , focused on datasets with strong object-in-the-center biases (e.g. Caltech-101). This simplified setting enabled clarity and focus on the MIL formulation, image features, and classifier design, but masked the vexing problem of finding a good initialization in data where such helpful biases are absent.