Deep Graph Matching Based on Neighbor Matching

The purpose of graph matching is to find the correspondence between nodes of two graphs. Existing graph matching models only consider the similarity between nodes when performing cross-graph convolutions, ignoring the consistency of the structure. To solve this problem, this paper proposes a new graph matching model that incorporates neighbor matching into the cross-graph convolution module. Neighbor matching calculates the attention weights for cross-graph convolution based on the local topology of nodes and neighbor dissimilarity. Because different neighbors have differing importance to the central node, neighbor matching first assigns aggregate weights for different neighboring nodes based on feature correlations. The cross-graph neighbor matching model then captures the distinctions among neighbors. Finally, the attention weights of cross-graph convolutions are jointly determined by the similarity between nodes and the consistency of their neighbors. This paper conducts comparative experiments on two public datasets. The experimental results show that on the Pascal Visual Object Classes(Pascal VOC) dataset, compared with the baseline model, the matching accuracy of the proposed model on 20 categories is increased by 0.9% on average; on the Spair-71k dataset, the average accuracy is increased by 1%.