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(in press) IEEE Trans. Pattern Analysis Machine Intelligence (TPAMI), 2007 Unsupervised Category Modeling, Recognition and Segmentation in Images
 

Summary: (in press) IEEE Trans. Pattern Analysis Machine Intelligence (TPAMI), 2007
Unsupervised Category Modeling, Recognition and Segmentation in 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 arbitrary (unlabeled) images contains frequent occurrences of 2D objects from an
unknown category. This paper is aimed at simultaneously solving the following related problems: (1)
unsupervised identification of photometric, geometric, and topological properties of multiscale regions
comprising instances of the 2D category; (2) learning a region-based structural model of the category in
terms of these properties; and (3) detection, recognition and segmentation 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 extract the maximally matching subtrees across the set, which
are taken as instances of the target category. The extracted subtrees are then fused into a tree-union
that represents the canonical category model. Detection, recognition, and segmentation of objects from
the learned category are achieved simultaneously by finding matches of the category model with the
segmentation tree of a new image. Experimental validation on benchmark datasets demonstrates the
robustness and high accuracy of the learned category models, when only a few training examples are
used for learning without any human supervision.

  

Source: Ahuja, Narendra - Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign
Todorovic, Sinisa - School of Electrical Engineering and Computer Science, Oregon State University

 

Collections: Computer Technologies and Information Sciences; Engineering