ACM Attributed Graph Clustering for Learning Classes of Images
M. Lozano, und F. Escolano. Graph Based Representations in Pattern Recognition(2003)
Zusammenfassung
In a previous work we have adapted the Asymmetric Clustering Model (ACM) to the domain of non-attributed graphs. We use our
Comb algorithm for graph matching, a population-based method which performs multi-point explorations of the discrete spaceof feasible solutions. Given this algorithm we define an incremental method to obtain a prototypical graph by fusing the elementsof the ensemble weighted by their prior probabilities of belonging to the class. Graph-matching and incremental fusion areintegrated in a EM clustering algorithm.
