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引用次数: 10

摘要

图匹配(GM)是计算机视觉和模式识别中的一个基本问题,它是在两个或多个图之间寻找顶点对应关系的问题。GM问题是一个离散组合优化问题。这个问题的性质是np困难的。首先详细介绍了图匹配的建模方法。介绍了二图匹配和多图匹配的最新发展。在两图匹配中,重点讨论了连续域算法,并简要介绍了离散域算法。在连续域方法中,我们详细解释了将问题从离散域转化为连续域的方法,以及每种算法中最先进的算法,包括谱方法、连续方法和深度学习方法。在两图匹配之后,介绍了几种典型的多图匹配算法。此外,还展示了图匹配在计算机视觉和多媒体中的应用研究活动。最后,对今后的研究方向进行了展望。
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A Survey On Graph Matching In Computer Vision
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.
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