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Extracting Subimages of an Unknown Category from a Set of Images Sinisa Todorovic and Narendra Ahuja
 

Summary: Extracting Subimages of an Unknown Category from a Set of Images
Sinisa Todorovic and Narendra Ahuja
Beckman Institute for Advanced Science and Technology
University of Illinois at Urbana-Champaign, U.S.A.
{sintod, ahuja}@vision.ai.uiuc.edu
Abstract
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 ev-
idence that a category is indeed present. The matched sub-

  

Source: Ahuja, Narendra - Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences; Engineering