Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 (2001)
Dec. 8, 2001 to Dec. 14, 2001
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
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.
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.