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
Citation
LIU, Zhiyong; QIAO, Hong; YANG, Xu; and HOI, Steven C. H..
Graph Matching by Simplified Convex-Concave Relaxation Procedure. (2014). International Journal of Computer Vision. 109, (3), 169-186.
Available at: https://ink.library.smu.edu.sg/sis_research/2286
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s11263-014-0707-7