Graph Matching by Simplified Convex-Concave Relaxation Procedure

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 applica...

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Veröffentlicht in:International journal of computer vision Jg. 109; H. 3; S. 169 - 186
Hauptverfasser: Liu, Zhi-Yong, Qiao, Hong, Yang, Xu, Hoi, Steven C. H.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.09.2014
Springer
Springer Nature B.V
Schlagworte:
Affinity
Algorithmics. Computability. Computer arithmetics
Algorithms
Annealing
Applied sciences
Artificial Intelligence
Computer Imaging
Computer Science
Computer science; control theory; systems
Computer vision
Exact sciences and technology
Formulations
Graph algorithms
Graph matching
Graphs
Image Processing and Computer Vision
Information retrieval. Graph
Mathematical models
Mathematics
Optimization
Optimization algorithms
Pattern Recognition
Pattern Recognition and Graphics
Permutations
State of the art
Studies
Theoretical computing
Vision
Combinatorial optimization
Deterministic annealing
Feature correspondence
Graduated optimization
Graph matching
Permutation matrix
Annealing
Experimental result
Adjacency matrix
Graph theory
Deterministic approach
ISSN:0920-5691, 1573-1405
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-014-0707-7