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...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Sinisa Todorovic, Narendra Ahuja
• Keywords: Computer Vision, CVPR 2006, Fewer Training Examples, Multiscale Image Segmentation, Region-based Structural Model, Regions Defining Objects, Unknown Category