Graph matching based on local and global information of the graph nodes

Abstract

Graph matching is an essential NP-problem in computer vision and pattern recognition. In this paper, we propose an approximate graph matching method. This method formulates the problem of computing the correspondences between two graphs as a problem of selecting nodes on an association graph. The nodes of the association graph represent candidate correspondences between the two original graphs. Our method first constructs an affinity matrix based on both the global and local information of the original graphs’ nodes. Each element of this matrix is used to measure the mutual consistency of a pair of nodes within the association graph. Our method then applies the reweighted random walks technique that preserves the one-to-one matching constraint to simulate random walks on the association graph and to iteratively compute a quasi-stationary distribution. To discretize this distribution, our method finally applies the Hungarian algorithm and obtains an approximate matching between the original two graphs. Experimental results demonstrate the effectiveness of our method for graph matching and the ability of our method for being robust to outlier and deformation noise.