A maximum diversity-based path sparsification for geometric graph matching

This paper presents an effective dissimilarity measure for geometric graphs representing shapes. The proposed dissimilarity measure is a distance that combines a sparsification of the geometric graph based on the maximum diversity problem and a new node embedding that captures the topological neighborhood of nodes. The sparsification step aims to reduce the size of the graph and to correct the misdistribution of nodes on the geometric graph induced by the noise of image handling. Experimental evaluation shows that the sparsification algorithm retains the form of the shapes while decreasing the number of processed nodes which reduces the overall matching time. Furthermore, the proposed node embedding and similarity measure give better performance in comparison with existing graph matching approaches. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper introduces an effective dissimilarity measure for geometric graphs representing shapes, which combines sparsification and node embedding methods to improve shape matching performance and reduce overall matching time. Experimental results demonstrate that the proposed approach outperforms existing methods in shape matching.