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Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 (2001)
Kauai, Hawaii
Dec. 8, 2001 to Dec. 14, 2001
ISSN: 1063-6919
ISBN: 0-7695-1272-0
pp: 912
Bin Luo , University of York
Antonio Robles-Kelly , University of York
Andrea Torsello , University of York
Richard C. Wilson , University of York
Edwin R. Hancock , University of York
ABSTRACT
This paper describes a probabilistic framework for graph-clustering. We commence from a set of pairwise distances between graph-structures. From this set of distances, we use a mixture model to characterize the pairwise affinity of the different graphs. We present an EM-like algorithmfor clustering the graphs by iteratively updating the elements of the affinity matrix. In the M-step we applying eigendcomposition to the affinity matrix to locate the principal clusters. In the M-step we update the affinity probabilities. We apply the resulting unsupervised clustering algorithm to two practical problems. The first of these involves locating shape-categories using shock trees extracted from 2D silhouettes. The second problem involves finding the view structure of a polyhedral object using the Delaunay triangulation of corner features.
INDEX TERMS
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CITATION

R. C. Wilson, E. R. Hancock, B. Luo, A. Torsello and A. Robles-Kelly, "A Probabilistic Framework for Graph Clustering," Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001(CVPR), Kauai, Hawaii, 2001, pp. 912.
doi:10.1109/CVPR.2001.990621
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