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Multimedia Tools and Applications

, Volume 74, Issue 17, pp 6989–7014 | Cite as

Person re-identification with content and context re-ranking

  • Qingming Leng
  • Ruimin HuEmail author
  • Chao Liang
  • Yimin Wang
  • Jun Chen
Article

Abstract

This paper proposes a novel and efficient re-ranking technque to solve the person re-identification problem in the surveillance application. Previous methods treat person re-identification as a special object retrieval problem, and compute the retrieval result purely based on a unidirectional matching between the probe and all gallery images. However, the correct matching may be not included in the top-k ranking result due to appearance changes caused by variations in illumination, pose, viewpoint and occlusion. To obtain more accurate re-identification results, we propose to reversely query every gallery person image in a new gallery composed of the original probe person image and other gallery person images, and revise the initial query result according to bidirectional ranking lists. The behind philosophy of our method is that images of the same person should not only have similar visual content, refer to content similarity, but also possess similar k-nearest neighbors, refer to context similarity. Furthermore, the proposed bidirectional re-ranking method can be divided into offline and online parts, where the majority of computation load is accomplished by the offline part and the online computation complexity is only proportional to the size of the gallery data set, which is especially suited to the real-time required video investigation task. Extensive experiments conducted on a series of standard data sets have validated the effectiveness and efficiency of our proposed method.

Keywords

Person re-identification Content and context similarities Bidirectional ranking Re-ranking 

Notes

Acknowledgments

This work was supported by the National Nature Science Foundation of China (61231015, 61172173, 61303114), the Major Science and Technology Innovation Plan of Hubei Province (2013AAA020), the Guangdong-Hongkong Key Domain Breakthrough Project of China (2012A090200007), the China Postdoctoral Science Foundation funded project (2013M530350), the Specialized Research Fund for the Doctoral Program of Higher Education (20130141120024), the Key Technology R&D Program of Wuhan (2013030409020109) and the President Fund of UCAS, and the Open Project Program of the National Laboratory of Pattern Recognition (NLPR).

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Qingming Leng
    • 1
  • Ruimin Hu
    • 1
    Email author
  • Chao Liang
    • 1
  • Yimin Wang
    • 1
  • Jun Chen
    • 1
  1. 1.National Engineering Research Center for Multimedia Software, School of ComputerWuhan UniversityWuhanPeople’s Republic of China

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