The hypergraph matching based on CCRP

Jun Zhou, Tao Wang, Yi Jin
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引用次数: 0

Abstract

Hypergraph matching plays an important role in the development of computer vision and has gained more and more attention. Although many algorithms have been generated, it is always difficult to solve the mathematical model of the hypergraph matching. Most algorithms use approximately local optimal solution to replace the global optimal solution of objective function. This will cause the hypergraph matching is unable to obtain the optimal solution in limited time. In order to solve this problem, we propose a new hypergraph matching algorithm based on CCRP to overcome the relaxation problem. We compare the proposed method with several excellent matching algorithms in three common benchmarks including Synthetic, CMU House and Willow. Our experimental results demonstrate the superiority of our new algorithm in matching accuracy and robustness against noise and deformation.
基于CCRP的超图匹配
超图匹配在计算机视觉的发展中起着重要的作用,越来越受到人们的重视。虽然已经产生了许多算法,但求解超图匹配的数学模型一直是一个难题。大多数算法使用近似局部最优解来代替目标函数的全局最优解。这将导致超图匹配无法在有限的时间内得到最优解。为了解决这一问题,我们提出了一种新的基于CCRP的超图匹配算法来克服松弛问题。我们将该方法与几种优秀的匹配算法在Synthetic、CMU House和Willow三个常见的基准测试中进行了比较。实验结果表明,该算法在匹配精度和对噪声和变形的鲁棒性方面具有优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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