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


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.


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



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).


  1. 1.
    Ali S, Javed O, Haering N, Kanade T (2010) Interactive retrieval of targets for wide area surveillance. In: Proceedings of the international conference on multimedia, pp 895–898Google Scholar
  2. 2.
    Baltieri D, Vezzani R, Cucchiara R (2011) 3dpes: 3D people dataset for surveillance and forensics. In: Proceedings of the 1st international ACM workshop on multimedia access to 3D human objectsGoogle Scholar
  3. 3.
    Dikmen M, Akbas E, Huang TS, Ahuja N (2010) Pedestrian recognition with a learned metric. In: ACCVGoogle Scholar
  4. 4.
    Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395Google Scholar
  5. 5.
    Friedkin NE (1998) A structural theory of social influence. Cambridge University PressGoogle Scholar
  6. 6.
    Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976zbMATHMathSciNetCrossRefGoogle Scholar
  7. 7.
    Farenzena M, Bazzani L, Perina A, Murino V, Cristani M (2010) Person re-identification by symmetry-driven accumulation of local features. In: CVPR, pp 2360–2367Google Scholar
  8. 8.
    Gheissari N, Sebastian TB, Tu PH, Rittscher J, Hartley R (2006) Person re-identification using spatiotemporal appearance. In: CVPR, vol 2, pp 1528–1535Google Scholar
  9. 9.
    Gray D, Brennan S, Tao H (2007) Evaluating appearance models for recognition, reacquisition, and tracking. In: PETSGoogle Scholar
  10. 10.
    Gong SG, Loy CC, Xiang T (2011) Security and surveillance. In: VAH, part 4, pp 455–472Google Scholar
  11. 11.
    Hirzer M, Beleznai C, Roth PM, Bischof H (2011) Person re-identification by descriptive and discriminative classification. In: SCIA, LNCS, vol 6688, pp 91–102Google Scholar
  12. 12.
    Huang J, Yang X, Fang X, Lin W, Zhang R (2011) Integrating visual saliency and consistency for re-ranking image search results. IEEE Trans Multimedia 13(4):653–661CrossRefGoogle Scholar
  13. 13.
    Kostinger M, Hirzer M, Wohlhart P, Roth P, Bischof H (2012) Large scale metric learning from equivalence constraints. In: CVPR, pp 2288–2295Google Scholar
  14. 14.
    Li W, Wu Y, Mukunoki M et al. (2012) Common-near-neighbor analysis for person re-identification. In: IEEE international conference on image processing (ICIP), pp 1621–1624Google Scholar
  15. 15.
    Leng Q, Hu R, Liang C, Wang Y, Chen J (2013) Bidirectional ranking for person re-identificationIn: Proceedings of IEEE computer society conference on multimedia and exposure (ICME)Google Scholar
  16. 16.
    Pedronette D, Guimares C, da S Torres R (2011) Exploiting contextual spaces for image re-ranking and rank aggregation. In: Proceedings of the 1st ACM international conference on multimedia retrievalGoogle Scholar
  17. 17.
    Rui Y, Huang TS, Ortega M, Mehrotra S (1998) Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans Circuits Syst Video Technol 8(5):644–655CrossRefGoogle Scholar
  18. 18.
    Shen X, Lin Z, Brandt J, Avidan S, Wu Y (2012) Object retrieval and localization with spatially-constrained similarity measure and k-NN re-ranking. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 3013–3020Google Scholar
  19. 19.
    Wang X, Doretto G, Sebastian T, Rittscher J, Tu P (2007) Shape and appearance context modeling. In: ICCV, pp 1–8Google Scholar
  20. 20.
    Weinberger KQ, Saul LK (2008) Fast solvers and efficient implementations for distance metric learning. In: ICMLGoogle Scholar
  21. 21.
    Wu Z, Ke Q, Sun J, Shum HY (2011) Scalable face image retrieval with identity-based quantization and multireference reranking. IEEE Trans Pattern Anal Machine Intell 33(10):1991–2001CrossRefGoogle Scholar
  22. 22.
    Xiang ZJ, Chen Q, Liu Y (2012) Person re-identification by fuzzy space color histogram. Multimed Tools Appl. doi: 10.1007/s11042-012-1286-7
  23. 23.
    You H, Chang E, Li B (2001) NNEW: nearest neighbor expansion by weighting in image database retrieval. In: Proceedings of IEEE international conference multimedia and exposure, pp 245–248Google Scholar
  24. 24.
    Zheng WS, Gong GS, Tao X (2011) Person re-identification by probabilistic relative distance comparison. In: CVPR, pp 649–656Google Scholar
  25. 25.
    Zhu C, Wen F, Sun J (2011) A rank-order distance based clustering algorithm for face tagging. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 481–488Google Scholar

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

Personalised recommendations