Graph matching based on fast normalized cut and multiplicative update mapping
Yang, Jing1,2; Yang, Xu2,4,5; Zhou, Zhang-Bing1,3; Liu, Zhi-Yong2,4,5
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2022-02-01
Volume122Pages:11
Corresponding AuthorYang, Xu(xu.yang@ia.ac.cn) ; Zhou, Zhang-Bing(zbzhou@cugb.edu.cn)
AbstractPoint correspondence is a fundamental problem in pattern recognition and computer vision, which can be tackled by graph matching. Since graph matching is basically an NP-complete problem, some approximate methods are proposed to solve it. Continuous relaxation offers an effective approximate method for graph matching problem. However, the discrete constraint is not taken into consideration in the optimization step. In this paper, a fast normalized cut based graph matching method is proposed, where the discrete constraint is introduced into the optimization step. Specifically, first a semidefinite positive affinity matrix based form objective function is constructed by introducing a regularization term which is related to the discrete constraint. Then the fast normalized cut algorithm is utilized to find the continuous solution. Last, the discrete solution of graph matching is obtained by a multiplicative update algorithm. Experiments on both synthetic points and real-world images validate the effectiveness of the proposed method by comparing it with the state-of-the-art methods. 0 2021 Elsevier Ltd. All rights reserved.
KeywordGraph matching Fast normalized cut Discrete constraint Multiplicative update
DOI10.1016/j.patcog.2021.108228
WOS KeywordALGORITHM ; OPTIMIZATION
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2020AAA0108902] ; National Natural Science Foundation (NSFC) of China[61973301] ; National Natural Science Foundation (NSFC) of China[61972020] ; National Natural Science Foundation (NSFC) of China[61633009] ; Beijing Science and Technology Plan Project[Z201100008320029] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation (NSFC) of China ; Beijing Science and Technology Plan Project ; Strategic Priority Research Program of Chinese Academy of Science
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000697675100005
PublisherELSEVIER SCI LTD
Sub direction classification模式识别基础
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46044
Collection多模态人工智能系统全国重点实验室_机器人理论与应用
Corresponding AuthorYang, Xu; Zhou, Zhang-Bing
Affiliation1.China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.TELECOM SudParis, Comp Sci Dept, F-91011 Evry, France
4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yang, Jing,Yang, Xu,Zhou, Zhang-Bing,et al. Graph matching based on fast normalized cut and multiplicative update mapping[J]. PATTERN RECOGNITION,2022,122:11.
APA Yang, Jing,Yang, Xu,Zhou, Zhang-Bing,&Liu, Zhi-Yong.(2022).Graph matching based on fast normalized cut and multiplicative update mapping.PATTERN RECOGNITION,122,11.
MLA Yang, Jing,et al."Graph matching based on fast normalized cut and multiplicative update mapping".PATTERN RECOGNITION 122(2022):11.
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