A Survey On Graph Matching In Computer Vision

被引:0
作者
Sun, Hui [1 ]
Zhou, Wenju [1 ]
Fei, Minrui [1 ]
机构
[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai, Peoples R China
来源
2020 13TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2020) | 2020年
基金
中国国家自然科学基金;
关键词
graph matching; optimization; quadratic assignment problem; deep learning; ALGORITHM;
D O I
10.1109/cisp-bmei51763.2020.9263681
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Graph matching (GM) which is the problem of finding vertex correspondence among two or multiple graphs is a fundamental problem in computer vision and pattern recognition. GM problem is a discrete combinatorial optimization problem. the property of this problem is NP-hard. Starting with a detailed introduction for modeling methods of graph matching. We walk through the recent development of two-graph matching and multi-graph matching. In two-graph matching, we focus on the continuous domain algorithms and briefly introduce the discrete domain algorithms. In the continuous domain method, we explain the method of transforming the problem from the discrete domain to the continuous domain and those state-of-the-arts algorithms in each type of algorithms in detail, including spectral methods, continuous methods, and deep learning methods. After two-graph matching, we introduce some typical multi-graph matching algorithms. In addition, the research activities of graph matching applications in computer vision and multimedia are displayed. In the end, several directions for future work are discussed.
引用
收藏
页码:225 / 230
页数:6
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