Learning Graph Matching Substitution Weights Based on the Ground Truth Node Correspondence

Abstract

In pattern recognition, it is usual to compare structured objects through attributed graphs in which nodes represent local parts and edges relations between them. Thus, each characteristic in the local parts is represented by different attributes on the nodes or edges. In this framework, the comparison between structured objects is performed through a distance between attributed graphs. If we want to correctly tune the distance and node correspondence between graphs, we need to add some weights on the node and edge attributes to gauge the importance of each local characteristic while defining the distance measure between graphs. In this paper, we present a method to learn the weights of each node and edge attribute such that the distance between the ground truth correspondence between graphs and the automatically obtained correspondence is minimized.