Person re-identification based on hierarchical bipartite graph matching

Yan Huang, Hao Sheng, Z. Xiong
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引用次数: 10

Abstract

This work proposes a novel person re-identification method based on Hierarchical Bipartite Graph Matching. Because human eyes observe person appearance roughly first and then goes further into the details gradually, our method abstracts person image from coarse to fine granularity, and finally into a three layer tree structure. Then, three bipartite graph matching methods are proposed for the matching of each layer between the trees. At the bottom layer Non-complete Bipartite Graph matching is proposed to collect matching pairs among small local regions. At the middle layer Semi-complete Bipartite Graph matching is used to deal with the problem of spatial misalignment between two person bodies. Complete Bipartite Graph matching is presented to refine the ranking result at the top layer. The effectiveness of our method is validated on the CAVIAR4REID and VIPeR datasets, and competitive results are achieved on both datasets.
基于层次二部图匹配的人物再识别
本文提出了一种基于层次二部图匹配的人物再识别方法。由于人眼先对人的外表进行粗略的观察,然后逐渐深入到细节,因此我们的方法将人的图像从粗粒度抽象到细粒度,最后形成三层树状结构。然后,提出了三种二部图匹配方法,用于树间各层的匹配。在底层提出非完全二部图匹配,收集小局部区域之间的匹配对。在中间层,采用半完全二部图匹配来处理两个人体之间的空间不对齐问题。采用完全二部图匹配的方法对顶层的排序结果进行细化。在CAVIAR4REID和VIPeR数据集上验证了该方法的有效性,并在两个数据集上取得了具有竞争力的结果。
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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