EAGER: Automated High Speed Object Category Modeling and Model Based Recognition, Segmentation, Clustering, and Classification

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This project explores new directions to solving the following problem. Given an image, determine whether and where specific objects, or objects from a specific category, appear in the image. Visual category is defined as earlier, namely, as a collection of objects which share characteristic features that are visually similar, and occur in similar configurations. The visual nature of objects sought is communicated through (training) data containing them, and estimated using machine learning. The approach consists of two main parts. First, it learns whether a given set of previously unseen images (including videos), say supplied by a user, contains any dominant themes, namely, subimages, that occur frequently and look similar. Second, given a set of categories automatically inferred during training and a new test image, the approach recognizes all occurrences in the image 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 require human supervision. The approach learns and recognizes categories as image hierarchies. The impact of the project includes accurate high-speed extraction of image regions, image representation by connected segmentation tree, robust image matching, unsupervised extraction of hierarchical category models, efficient recognition of a large number of categories, unsupervised estimation of perceptually salient, relevance weights of subcategory detections to category recognition, and generalization of the proposed approach to extraction of texture elements. More broadly, the proposed approach is useful for applications in search engines, surveillance, video analytics, monitoring and data mining.