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Coupled-View Based Ranking Optimization for Person Re-identification

  • Mang Ye
  • Jun Chen
  • Qingming Leng
  • Chao Liang
  • Zheng Wang
  • Kaimin Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8935)

Abstract

Person re-identification aims to match different persons observed in non-overlapping camera views. Researchers have proposed many person descriptors based on global or local descriptions, while both of them have achieved satisfying matching results, however, their ranking lists usually vary a lot for the same query person. These motivate us to investigate an approach to aggregate them to optimize the original matching results. In this paper, we proposed a coupled-view based ranking optimization method through cross KNN rank aggregation and graph-based re-ranking to revise the original ranking lists. Its core assumption is that the images of the same person should share the similar visual appearance in both global and local views. Extensive experiments on two datasets show the superiority of our proposed method with an average improvement of 20-30% over the state-of-the-art methods at CMC@1.

Keywords

Coupled-view Ranking optimization Person re-identification 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mang Ye
    • 1
  • Jun Chen
    • 1
    • 2
  • Qingming Leng
    • 3
  • Chao Liang
    • 1
    • 2
  • Zheng Wang
    • 1
  • Kaimin Sun
    • 4
  1. 1.National Engineering Research Center for Multimedia Software, School of ComputerWuhan UniversityWuhanChina
  2. 2.Research Institute of Wuhan University in ShenzhenChina
  3. 3.School of Information Science and TechnologyJiujiang UniversityChina
  4. 4.State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote SensingWuhan UniversityChina

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