A Continuation Method for Graph Matching Based Feature Correspondence | |
Yang, Xu3; Liu, Zhi-Yong1,2,3; Qiao, Hong1,2,3 | |
刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE |
2020-08-01 | |
卷号 | 42期号:8页码:1809-1822 |
关键词 | Feature correspondence graph matching continuous method continuation method combinatorial optimization |
ISSN号 | 0162-8828 |
DOI | 10.1109/TPAMI.2019.2903483 |
通讯作者 | Liu, Zhi-Yong(zhiyong.liu@ia.ac.cn) |
英文摘要 | 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. |
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] |
WOS关键词 | OPTIMIZATION |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | IEEE COMPUTER SOC |
WOS记录号 | WOS:000545415400001 |
资助机构 | National Natural Science Foundation (NSFC) of China ; National Key R\&D Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40004] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
通讯作者 | Liu, Zhi-Yong |
作者单位 | 1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China |
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. |
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