A novel graph matching method based on multiple information of the graph nodes

Graph matching is a fundamental NP-problem in computer graphics and computer vision. In this work, we present an approximate graph matching method. Given two graphs to be matched, the proposed method first constructs an association graph to convert the problem of graph matching into a problem of selecting nodes on the constructed graph. The nodes of the association graph represent candidate correspondences between the two original graphs. An affinity matrix is then computed based on the local, intermediate and global information of the original graphs' nodes, each element of which is used to measure the mutual consistency of a correspondence pair within the association graph. Updating the affinity of each correspondence pair with the affinities of relevant correspondences, our method then utilizes the reweighted random walks strategy to simulate random walks on the association graph and to iteratively obtain a quasi-stationary distribution. Finally, our method applies the Hungarian algorithm to discretize the distribution. Experimental results on four common datasets verify the effectiveness of the proposed method.