Clustering of attributed graphs and unsupervised synthesis of function-described graphs

Function-described graphs (FDGs) have been introduced by the authors as a representation of an ensemble of attributed graphs (AGs) for structural pattern recognition as an alternative to first-order random graphs. 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 synthesising the FDGs that describe the classes