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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Bibliographic Details
Published in:	International journal of computer vision Vol. 109; no. 3; pp. 169 - 186
Main Authors:	Liu, Zhi-Yong, Qiao, Hong, Yang, Xu, Hoi, Steven C. H.
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.09.2014 Springer Springer Nature B.V
Subjects:	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 Access:	Get full text
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