RI-Small: Discovery, Modeling and Recognition of Objects in Image Sets

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This project is about automated, visual object recognition. It is aimed at a computational approach which has two parts. First, it learns whether a given set of previously unseen images, say supplied by a user, contains any dominant themes, namely, subimages, that occur frequently and look similar. Such themes, and the associated subimages, are called categories and objects, respectively. Second, given a set of categories automatically inferred during the aforementioned training, and a new, test image, the approach recognizes all occurrences in the image of objects belonging to any of the learned categories. It delineates each such object in the image, and labels it with its category name. Both learning and subsequent recognition do not need human supervision. The subimages defining a category can be small or large, simple or complex. It is reasonable to expect that low-complexity categories, e.g., containing small/few/simple subimages are more common in real-world images. For example, the simple category of elongated shapes occurs as a part of legged animals, stools and scissors. More complex categories consist of large/many/complicated regions and are less common. Simple categories, e.g., the ``leg'' are thus shared by more complex ones, e.g., all legged animals, and, in turn, ``leg'' is an articulated combination of the category of elongated shapes (limbs). Therefore, category representation can be made easier by expressing it as a configuration of simpler categories, instead of subimages directly, thus yielding a hierarchical, subpart model. Accordingly, the proposed approach learns and recognizes categories as image hierarchies. The use of hierarchical embedding of regions as the defining image features results in several advantages the proposed approach offers over existing other methods which mostly use local features: (1) The proposed approach requires no supervision, e.g., labeling or segmenting of training images, or other input parameters from the user. (2) It simultaneously provides category detection and high-accuracy segmentation. (3) Training is feasible with very few examples, and not all training images must contain objects from the categories. (4) The use of hierarchical models makes explicit the relationship of a specific category to other categories of similar, lower and higher complexities; it also serves as a semantic explanation of why a category is detected when detected. Expected major contributions of the work include computational formulations of: (1) Accurate extraction of image regions; (2) Image representation by connected segmentation tree; (3) Robust image matching amidst structural noise in images; (4) Unsupervised extraction of hierarchical category models; (5) Efficient recognition of a large number of categories; (6) Unsupervised estimation of the relevance weights of subcategory detections to category recognition, and (7) Generalization of the proposed approach to extraction of texture elements, as an example of how the proposed work may impact other challenging vision problems involving hierarchy.The progress made on this project can be seen at the website: http://vision.ai.uiuc.edu/ahuja.html