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15th International Conference on Pattern Recognition (ICPR'00) - Volume 2   p. 6022
Clustering of Attributed Graphs and Unsupervised Synthesis of Function-Described Graphs
Alberto Sanfeliu, University Politècnica de Catalunya
Rene Alquezar, University Politècnica de Catalunya
Francesc Serratosa, Universitat Rovira i Virgili

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2000.906248
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Abstract
Function-Described Graphs (FDGs) have been introduced by the authors as a representation of an ensemble of Attributed Graphs (AGs) for structural pattern recognition alternative to first-order random graphs. Both optimal and approximate algorithms for error-tolerant graph matching, which use a distance measure between AGs and FDGs, have been reported elsewhere. In addition, the supervised synthesis of FDGs from a set of graphs with a common labeling has been addressed previously. In this paper, the unsupervised synthesis of FDGs is studied in the context of clustering a set of AGs and obtaining an FDG model for each cluster. Two algorithms based on incremental and hierarchical clustering, respectively, are proposed, which are parameterized by a graph matching method. Results on 3D-object recognition show that these algorithms are effective for clustering a set of AGs and synthesizing the FDGs that describe the classes.
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Citation:  Alberto Sanfeliu, Rene Alquezar, Francesc Serratosa, "Clustering of Attributed Graphs and Unsupervised Synthesis of Function-Described Graphs," icpr, p. 6022,  15th International Conference on Pattern Recognition (ICPR'00) - Volume 2,  2000