ACM Attributed Graph Clustering for Learning Classes of Images

Abstract

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 space of feasible solutions. Given this algorithm we define an incremental method to obtain a prototypical graph by fusing the elements of the ensemble weighted by their prior probabilities of belonging to the class. Graph-matching and incremental fusion are integrated in a EM clustering algorithm.

In this paper, we adapt the latter ACM clustering model to deal with attributed graphs, where these attributes are probability density functions associated to nodes and edges. In order to do so, we modify the incremental method for obtaining a prototypical graph to update these pdf’s provided that they are statistically compatible with those of the corresponding nodes and edges. This graph-clustering approach is successfully tested in the domain of Mondrian images (images built on rectangular patches of colored textures) because our final purpose is to the unsupervised learning of image classes.