A Continuation Method for Graph Matching Based Feature Correspondence

doi:10.1109/TPAMI.2019.2903483

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	A Continuation Method for Graph Matching Based Feature Correspondence
	Yang, Xu1 ; Liu, Zhi-Yong1,2,3 ; Qiao, Hong1,2,3
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
	2020-08-01
卷号	42 期号:8 页码:1809-1822
通讯作者	Liu, Zhi-Yong(zhiyong.liu@ia.ac.cn)
摘要	Feature correspondence lays the foundation for many computer vision and image processing tasks, which can be well formulated and solved by graph matching. Because of the high complexity, approximate methods are necessary for graph matching, and the continuous relaxation provides an efficient approximate scheme. But there are still many problems to be settled, such as the highly nonconvex objective function, the ignorance of the combinatorial nature of graph matching in the optimization process, and few attention to the outlier problem. Focusing on these problems, this paper introduces a continuation method directly targeting at the combinatorial optimization problem associated with graph matching. Specifically, first a regularization function incorporating the original objective function and the discrete constraints is proposed. Then a continuation method based on Gaussian smoothing is applied to it, in which the closed forms of relevant functions with respect to the outlier distribution are deduced. Experiments on both synthetic data and real world images validate the effectiveness of the proposed method.
关键词	Feature correspondence graph matching continuous method continuation method combinatorial optimization
DOI	10.1109/TPAMI.2019.2903483
关键词[WOS]	OPTIMIZATION
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation (NSFC) of China[61633009] ; National Natural Science Foundation (NSFC) of China[61503383] ; National Natural Science Foundation (NSFC) of China[U1613213] ; National Natural Science Foundation (NSFC) of China[61627808] ; National Natural Science Foundation (NSFC) of China[91648205] ; National Natural Science Foundation (NSFC) of China[U1509212] ; National Key R\&D Program of China[2016YFC0300801] ; National Key R\&D Program of China[2017YFB1300202] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB32000000]
项目资助者	National Natural Science Foundation (NSFC) of China ; National Key R\&D Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000545415400001
出版者	IEEE COMPUTER SOC
七大方向——子方向分类	模式识别基础
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/40005
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Liu, Zhi-Yong
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yang, Xu,Liu, Zhi-Yong,Qiao, Hong. A Continuation Method for Graph Matching Based Feature Correspondence[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2020,42(8):1809-1822.
APA	Yang, Xu,Liu, Zhi-Yong,&Qiao, Hong.(2020).A Continuation Method for Graph Matching Based Feature Correspondence.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,42(8),1809-1822.
MLA	Yang, Xu,et al."A Continuation Method for Graph Matching Based Feature Correspondence".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 42.8(2020):1809-1822.