Extracting Subimages of an Unknown Category from a Set of Images

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Extracting Subimages of an Unknown Category from a Set of Images
Suppose a set of images contains frequent occurrences of objects from an unknown category. This paper is aimed at simultaneously solving the following related problems: (1) unsupervised identification of photometric, geometric, and topological (mutual containment) properties of multiscale regions defining objects in the category; (2) learning a region-based structural model of the category in terms of these properties from a set of training images; and (3) segmentation and recognition of objects from the category in new images. To this end, each image is represented by a tree that captures a multiscale image segmentation. The trees are matched to find the maximally matching subtrees across the set, the existence of which is itself viewed as evidence that a category is indeed present. The matched subtrees are fused into a canonical tree, which represents the learned model of the category. Recognition of objects in a new image and image segmentation delineating all object parts are achi...
Sinisa Todorovic, Narendra Ahuja
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2006
Where CVPR
Authors Sinisa Todorovic, Narendra Ahuja
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