Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2014

Abstract

The convex and concave relaxation procedure (CCRP) was recently proposed and exhibited state-of-the-art performance on the graph matching problem. However, CCRP involves explicitly both convex and concave relaxations which typically are difficult to find, and thus greatly limit its practical applications. In this paper we propose a simplified CCRP scheme, which can be proved to realize exactly CCRP, but with a much simpler formulation without needing the concave relaxation in an explicit way, thus significantly simplifying the process of developing CCRP algorithms. The simplified CCRP can be generally applied to any optimizations over the partial permutation matrix, as long as the convex relaxation can be found. Based on two convex relaxations, we obtain two graph matching algorithms defined on adjacency matrix and affinity matrix, respectively. Extensive experimental results witness the simplicity as well as state-of-the-art performance of the two simplified CCRP graph matching algorithms.

Keywords

Graph matching, Combinatorial optimization, Deterministic annealing, Graduated optimization, Feature correspondence

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

International Journal of Computer Vision

Volume

109

Issue

3

First Page

169

Last Page

186

ISSN

0920-5691

Identifier

10.1007/s11263-014-0707-7

Publisher

Springer

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1007/s11263-014-0707-7

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