保留全局和局部结构的团图匹配

Weizhi Nie, Anan Liu, Zan Gao, Yuting Su
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引用次数: 60

摘要

本文首先提出了团图的概念,在此基础上提出了一种保留全局结构和局部结构的团图匹配方法。特别地,我们给出了对应于两个潜在变量、原始图中的团团信息和一对一匹配约束下的成对团团对应关系的团团图匹配目标函数。由于目标函数对两个潜在变量不是联合凸的,我们将其分解为两个连续的步骤进行优化:1)通过保留局部一元和两两对应来度量团对团的相似性;2)保持全局团对团对应关系的图对图相似性度量。在合成数据和真实图像上进行的大量实验表明,该方法在噪声和异常值同时存在的情况下优于代表性方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clique-graph matching by preserving global & local structure
This paper originally proposes the clique-graph and further presents a clique-graph matching method by preserving global and local structures. Especially, we formulate the objective function of clique-graph matching with respective to two latent variables, the clique information in the original graph and the pairwise clique correspondence constrained by the one-to-one matching. Since the objective function is not jointly convex to both latent variables, we decompose it into two consecutive steps for optimization: 1) clique-to-clique similarity measure by preserving local unary and pairwise correspondences; 2) graph-to-graph similarity measure by preserving global clique-to-clique correspondence. Extensive experiments on the synthetic data and real images show that the proposed method can outperform representative methods especially when both noise and outliers exist.
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