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.
Graph matching, Combinatorial optimization, Deterministic annealing, Graduated optimization, Feature correspondence
Computer Sciences | Databases and Information Systems
Data Management and Analytics
International Journal of Computer Vision
LIU, Zhiyong; QIAO, Hong; YANG, Xu; and HOI, Chu Hong.
Graph Matching by Simplified Convex-Concave Relaxation Procedure. (2014). International Journal of Computer Vision. 109, (3), 169-186. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2286
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