Extracting Subimages of an Unknown Category from a Set of Images.

摘要

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 multi-scale 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 sub-trees 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 achieved simultaneously by finding matches of the model with subtrees of the new image. Experimental comparison with state-of- the-art methods shows that the proposed approach has similar recognition and superior localization performance while it uses fewer training examples.