Skip to main content

Local Descriptors Encoded by Fisher Vectors for Person Re-identification

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7583)

Abstract

This paper proposes a new descriptor for person re-identification building on the recent advances of Fisher Vectors. Specifically, a simple vector of attributes consisting in the pixel coordinates, its intensity as well as the first and second-order derivatives is computed for each pixel of the image. These local descriptors are turned into Fisher Vectors before being pooled to produce a global representation of the image. The so-obtained Local Descriptors encoded by Fisher Vector (LDFV) have been validated through experiments on two person re-identification benchmarks (VIPeR and ETHZ), achieving state-of-the-art performance on both datasets.

Keywords

  • Gaussian Mixture Model
  • Local Binary Pattern
  • IEEE Conf
  • Local Descriptor
  • Pairwise Constraint

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Farenzena, M., Bazzani, L., Perina, A., Murino, V., Cristani, M.: Person re-identification by symmetry-driven accumulation of local features. In: Proc. IEEE Conf. on Comp. Vision and Pattern Recognition (2010)

    Google Scholar 

  2. Oreifej, O., Mehran, R., Shah, M.: Human identity recognition in aerial images. In: Proc. IEEE Conf. on Comp. Vision and Pattern Recognition (2010)

    Google Scholar 

  3. Schwartz, W., Davis, L.: Learning discriminative appearance based models using partial least squares. In: Brazilian Symp. on Comp. Graphics and Im. Proc. (2009)

    Google Scholar 

  4. Prosser, B., Zheng, W., Gong, S., Xiang, T.: Person re-identification by support vector ranking. In: BMVC (2010)

    Google Scholar 

  5. Zhang, Y., Li, S.: Gabor-LBP based region covariance descriptor for person re-identification. In: Int. Conf. on Image and Graphics, pp. 368–371 (2011)

    Google Scholar 

  6. 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)

    CrossRef  Google Scholar 

  7. Bak, S., Corvee, E., Bremond, F., Thonnat, M.: Person re-identification using haar-based and DCD-based signature. In: Proc. Int. Workshop on Activity Monitoring by Multi-Camera Surveillance Systems (2010)

    Google Scholar 

  8. Gheissari, N., Sebastian, T., Tu, P., Rittscher, J., Hartley, R.: Person re-identification using spatiotemporal appearance. In: Proc. IEEE Conf. on Comp. Vision and Pattern Recognition (2006)

    Google Scholar 

  9. Kai, J., Bodensteiner, C., Arens, M.: Person re-identification in multi-camera networks. In: Proc. IEEE CVPR Workshops (2011)

    Google Scholar 

  10. Zheng, W., Gong, S., Xiang, T.: Associating groups of people. In: BMVC (2009)

    Google Scholar 

  11. Cheng, D.S., Cristani, M., Stoppa, M., Bazzani, L., Murino, V.: Custom pictorial structures for re-identification. In: BMVC (2011)

    Google Scholar 

  12. Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. In: Proc. IEEE Conf. on Comp. Vision and Pattern Recognition (2003)

    Google Scholar 

  13. Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: Proc. IEEE Conf. on Comp. Vision and Pattern Recognition (2007)

    Google Scholar 

  14. Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher Kernel for Large-Scale Image Classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)

    CrossRef  Google Scholar 

  15. Perronnin, F., Liu, Y., Sánchez, J., Poirier, H.: Large-scale image retrieval with compressed Fisher vectors. In: Proc. IEEE Conf. on Comp. Vision and Pattern Recognition (2010)

    Google Scholar 

  16. Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2007)

    Google Scholar 

  17. Weinberger, K., Saul, L.: Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research 10, 207–244 (2009)

    MATH  Google Scholar 

  18. Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: Proc. International Conference on Machine Learning, pp. 209–216 (2007)

    Google Scholar 

  19. Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? metric learning approaches for face identification. In: Proc. IEEE International Conference on Computer Vision (2009)

    Google Scholar 

  20. Mignon, A., Jurie, F.: PCCA: a new approach for distance learning from sparse pairwise constraints. In: Proc. IEEE Conf. on Comp. Vision and Pattern Recognition (2012)

    Google Scholar 

  21. Tuzel, O., Porikli, F., Meer, P.: Pedestrian detection via classification on riemannian manifolds. IEEE Trans. on PAMI 30, 1713–1727 (2008)

    CrossRef  Google Scholar 

  22. Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: BMVC (2011)

    Google Scholar 

  23. Ess, A., Leibe, B., Schindler, K., van Gool, L.: A mobile vision system for robust multi-person tracking. In: Proc. IEEE Conf. on Comp. Vision and Pattern Recognition (2008)

    Google Scholar 

  24. Moon, H., Phillips, P.: Computational and performance aspects of PCA-based face-recognition algorithms. Perception 30, 303–321 (2001)

    CrossRef  Google Scholar 

  25. Fisher, R.A.: The use of multiple measures in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)

    Google Scholar 

  26. Zheng, W., Gong, S., Xiang, T.: Person re-identification by probabilistic relative distance comparison. In: Proc. IEEE Conf. on Comp. Vision and Pattern Recognition (2011)

    Google Scholar 

  27. Globerson, A., Roweis, S.: Metric learning by collapsing classes. In: Advances in Neural Information Processing Systems (2006)

    Google Scholar 

  28. 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)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ma, B., Su, Y., Jurie, F. (2012). Local Descriptors Encoded by Fisher Vectors for Person Re-identification. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33863-2_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-33863-2_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33862-5

  • Online ISBN: 978-3-642-33863-2

  • eBook Packages: Computer ScienceComputer Science (R0)