Pacific Rim Conference on Multimedia

Advances in Multimedia Information Processing -- PCM 2015 pp 75-84 | Cite as

Adaptive Margin Nearest Neighbor for Person Re-Identification

  • Lei Yao
  • Jun Chen
  • Yi Yu
  • Zheng Wang
  • Wenxin Huang
  • Mang Ye
  • Ruimin Hu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9314)

Abstract

Person re-identification is a challenging issue due to large visual appearance changes caused by variations in viewpoint, lighting, background clutter and occlusion among different cameras. Recently, Mahalanobis metric learning methods, which aim to find a global, linear transformation of the feature space between cameras [1, 2, 3, 4], are widely used in person re-identification. In order to maximize the inter-class variation, general Mahalanobis metric learning methods usually push impostors (i.e., all negative samples that are nearer than the target neighbors) to a fixed threshold distance away, treating all these impostors equally without considering their diversity. However, for person re-identification, the discrepancies among impostors are useful for refining the ranking list. Motivated by this observation, we propose an Adaptive Margin Nearest Neighbor (AMNN) method for person re-identification. AMNN aims to take unequal treatment to each samples impostors by pushing them to adaptive variable margins away. Extensive comparative experiments conducted on two standard datasets have confirmed the superiority of the proposed method.

Keywords

Person re-identification Metric learning LMNN Adaptive margin 

Notes

Acknowledgement

The research was supported by National Nature Science Foundation of China (No. 61231015, No. 61170023, No. 61172173, No. 61303114). National High Technology Research and Development Program of China (863 Program, No. 2015AA016306). Technology Research Program of Ministry of Public Security (No. 2014JSYJA016). The EUFP7 QUICK project under Grant Agreement (No. PIRSES-GA-2013-612652). Major Science and Technology Innovation Plan of Hubei Province (No. 2013AAA020). Internet of Things Development Funding Project of Ministry of industry in 2013 (No. 25). China Postdoctoral Science Foundation funded project (2013M530350). Specialized Research Fund for the Doctoral Program of Higher Education (No. 20130141120024). Nature Science Foundation of Hubei Province (2014CFB712). The Fundamental Research Funds for the Central Universities (2042014kf0250, 2014211020203). Jiangxi Youth Science Foundation of China(Grant No. 20151BAB217013).

References

  1. 1.
    Dikmen, M., Akbas, E., Huang, T.S., Ahuja, N.: Pedestrian recognition with a learned metric. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part IV. LNCS, vol. 6495, pp. 501–512. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  2. 2.
    Zheng, W.-S., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)Google Scholar
  3. 3.
    Kostinger, M., Hirzer, M., Wohlhart, P., Roth, P.M., Bischof, H.: Large scale metric learning from equivalence constraints. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)Google Scholar
  4. 4.
    Wang, Y., Hu, R., Liang, C., Zhang, C., Leng, Q.: Camera compensation using feature projection matrix for person re-identification. In: IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) (2014)Google Scholar
  5. 5.
    Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)Google Scholar
  6. 6.
    Baltieri, D., Vezzani, R., et al.: Learning articulated body models for people re-identification. In: ACM Multimedia (MM) (2013)Google Scholar
  7. 7.
    Deng, Y., Luo, P., Loy, C.C., Tang, X.: Pedestrian attribute recognition at far distance. In: ACM Multimedia (MM) (2014)Google Scholar
  8. 8.
    Leng, Q., Hu, R., Liang, C., Wang, Y., Chen, J.: Person re-identification with content and context re-ranking. In: Multimedia Tools and Applications (MTA) (2014)Google Scholar
  9. 9.
    Wang, Z., Hu, R., Liang, C., Leng, Q., Sun, K.: Region-based interactive ranking optimization for person re-identification. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, C.-K., Huet, B., Ngo, C.-W. (eds.) PCM 2014. LNCS, vol. 8879, pp. 1–10. Springer, Heidelberg (2014) Google Scholar
  10. 10.
    Park, U., Jain, A., Kitahara, I., Kogure, K., Hagita, N.: Vise: visual search engine using multiple networked cameras. In: International Conference on Pattern Recognition (ICPR) (2006)Google Scholar
  11. 11.
    Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 262–275. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  12. 12.
    Gheissari, N., Sebastian, T., Hartley, R.: Person reidentification using spatiotemporal appearance. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2006)Google Scholar
  13. 13.
    Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: IEEE International Conference on Computer Vision (ICCV) (2007)Google Scholar
  14. 14.
    Hu, W., Hu, M., Zhou, X., Lou, J., Tan, T., Maybank, S.: Principal axis-based correspondence between multiple cameras for people tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) (2006)Google Scholar
  15. 15.
    Dollar, P., Tu, Z., Tao, H., Belongie, S.: Feature mining for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2007)Google Scholar
  16. 16.
    Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. In: Advances in Neural Information Processing Systems (NIPS) (2006)Google Scholar
  17. 17.
    Weinberger, K.Q., Saul, L.K.: Fast solvers and efficient implementations for distance metric learning. In: international Conference on Machine Learning (ICML) (2008)Google Scholar
  18. 18.
    Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: international conference on Machine learning (ICML) (2007)Google Scholar
  19. 19.
    Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? Metric learning approaches for face identification. In: IEEE International Conference on Computer Vision (ICCV) (2009)Google Scholar
  20. 20.
    Li, X., Tao, D., Jin, L., Wang, Y., Yuan, Y.: Person re-identification by regularized smoothing kiss metric learning. In: IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) (2013)Google Scholar
  21. 21.
    Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS) (2007)Google Scholar
  22. 22.
    Li, W., Zhao, R., Wang, X.: Human reidentification with transferred metric learning. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 31–44. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  23. 23.
    Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: An overview with application to learning methods. In: Neural Computation, Canonical Correlation Analysis (2004)Google Scholar
  24. 24.
    Li, W., Wang, X.: Locally aligned feature transforms across views. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)Google Scholar
  25. 25.
    Liu, K., Guo, X., Zhao, Z., Cai, A.: Person re-identification using matrix complex. In: IEEE International Conference on Image Processing (ICIP) (2013)Google Scholar
  26. 26.
    Wang, X., Doretto, G., Sebastian, T., Rittscher, J., Tu, P.: Shape and appearance context modeling. In: IEEE International Conference on Computer Vision (ICCV) (2007)Google Scholar
  27. 27.
    Yang, Y., Yang, J., Yan, J., Liao, S., Yi, D., Li, S.Z.: Salient color names for person re-identification. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 536–551. Springer, Heidelberg (2014) Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lei Yao
    • 1
  • Jun Chen
    • 1
    • 2
  • Yi Yu
    • 3
  • Zheng Wang
    • 1
  • Wenxin Huang
    • 1
  • Mang Ye
    • 1
  • Ruimin Hu
    • 1
    • 2
  1. 1.National Engineering Research Center for Multimedia Software, School of ComputerWuhan UniversityWuhanChina
  2. 2.Collaborative Innovation Center of Geospatial TechnologyWuhanChina
  3. 3.National Institute of InformaticsChiyodaJapan

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