CASIA OpenIR
Sub-hypergraph matching based on adjacency tensor
Yang, Jing1,2; Yang, Xu2; Zhou, Zhang-Bing1,3; Liu, Zhi-Yong2,4,5
Source PublicationCOMPUTER VISION AND IMAGE UNDERSTANDING
ISSN1077-3142
2019-06-01
Volume183Pages:1-10
Corresponding AuthorYang, Xu() ; Zhou, Zhang-Bing(zhangbing.zhou@gmail.com)
AbstractPoint correspondence problem is an important problem in pattern recognition and computer vision, which can be solved by graph matching. Recently, high order graph matching methods have attracted much attention due to their robustness to geometric transformations. Since high order graph matching usually suffers from high complexity, we previously proposed an adjacency tensor based algorithm, which effectively reduced the complexity of high order graph matching, especially high storage complexity. However, this method can only be applied to equal sized hypergraphs, and it cannot be directly extended to hypergraphs with outliers which are common in real world tasks. Aiming at this problem, in this paper we propose a third order subgraph matching method by extending our previous method to deal with partial point correspondence problem with outliers. Specifically, first a novel objective function focusing on the outlier problem is proposed, by encoding the attributes in a hypergraph with an adjacency tensor, and representing vertex assignments with a partial permutation matrix. Then the objective function is transformed and relaxed to a tractable matrix form and solved by a gradient based optimization algorithm. Consequently, the proposed algorithm can not only tackle the outlier vertices in the hypergraphs, but also involve the same low computational and storage complexities with our previous algorithm. Both synthetic data and real image comparisons with the state-of-the-art methods validate the effectiveness of the proposed method.
KeywordHypergraph matching Subgraph matching High order structure Adjacency tensor
DOI10.1016/j.cviu.2019.03.003
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2016YFC0300801] ; National Key R&D Program of China[2017YFB1300202] ; National Natural Science Foundation (NSFC) of China[61503383] ; National Natural Science Foundation (NSFC) of China[61633009] ; National Natural Science Foundation (NSFC) of China[U1613213] ; National Natural Science Foundation (NSFC) of China[61773047] ; National Natural Science Foundation (NSFC) of China[61772479] ; National Natural Science Foundation (NSFC) of China[61662021]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation (NSFC) of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000469164200001
PublisherACADEMIC PRESS INC ELSEVIER SCIENCE
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24391
Collection中国科学院自动化研究所
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 AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yang, Jing,Yang, Xu,Zhou, Zhang-Bing,et al. Sub-hypergraph matching based on adjacency tensor[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2019,183:1-10.
APA Yang, Jing,Yang, Xu,Zhou, Zhang-Bing,&Liu, Zhi-Yong.(2019).Sub-hypergraph matching based on adjacency tensor.COMPUTER VISION AND IMAGE UNDERSTANDING,183,1-10.
MLA Yang, Jing,et al."Sub-hypergraph matching based on adjacency tensor".COMPUTER VISION AND IMAGE UNDERSTANDING 183(2019):1-10.
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