The 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 algorithm for clustering the graphs by iteratively updating the elements of the affinity matrix. In the M-step we apply 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.


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    Title :

    A probabilistic framework for graph clustering


    Contributors:
    Bin Luo, (author) / Robles-Kelly, A. (author) / Torsello, A. (author) / Wilson, R.C. (author) / Hancock, E.R. (author)


    Publication date :

    2001-01-01


    Size :

    764104 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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