Unsupervised Local Regressive Attributes for Pedestrian Re-identification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


Discovering of attributes is a challenging task in computer vision due to uncertainty about the attributes, which is caused mainly by the lack of semantic meaning in image parts. A usual scheme for facing attribute discovering is to divide the feature space using binary variables. Moreover, we can assume to know the attributes and by using expert information we can give a degree of attribute beyond only two values. Nonetheless, a binary variable could not be very informative, and we could not have access to expert information. In this work, we propose to discover linear regressive codes using image regions guided by a supervised criteria where the obtained codes obtain better generalization properties. We found that the discovered regressive codes can be successfully re-used in other visual datasets. As a future work, we plan to explore richer codification structures than lineal mapping considering efficient computation.


Attribute discovery Pedestrian re-identification Unsupervised learning 


  1. 1.
    Parikh, D., Grauman, K.: Relative attributes. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 503–510 (2011)Google Scholar
  2. 2.
    Rastegari, M., Farhadi, A., Forsyth, D.: Attribute discovery via predictable discriminative binary codes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 876–889. Springer, Heidelberg (2012). CrossRefGoogle Scholar
  3. 3.
    Lobel, H., Vidal, R., Soto, A.: Hierarchical joint max-margin learning of mid and top level representations for visual recognition. In: Proceedings of International Conference on Computer Vision (2013)Google Scholar
  4. 4.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, MM 2014, pp. 675–678. ACM, New York (2014)Google Scholar
  5. 5.
    Genuer, R., Poggi, J.M., Tuleau-Malot, C.: Variable selection using random forests. Pattern Recogn. Lett. 31(14), 2225–2236 (2010)CrossRefGoogle Scholar
  6. 6.
    Hu, Y., Yi, D., Liao, S., Lei, Z., Li, S.Z.: Cross dataset person re-identification. In: Jawahar, C.V., Shan, S. (eds.) ACCV 2014. LNCS, vol. 9010, pp. 650–664. Springer, Cham (2015). Google Scholar
  7. 7.
    Ma, A., Yuen, P., Li, J.: Domain transfer support vector ranking for person re-identification without target camera label information. In: Proceedings of International Conference on Computer Vision (2013)Google Scholar
  8. 8.
    Yi, D., Lei, Z., Li, S.: Deep metric learning for practical person re-identification. In: Proceedings of International Conference on Pattern Recognition (2014)Google Scholar
  9. 9.
    Globerson, A., Roweis, S.T.: Metric learning by collapsing classes. In: Advances in Neural Information Processing Systems (2005)Google Scholar
  10. 10.
    Weinberger, K.Q., Blitzer, J., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. In: Advances in Neural Information Processing Systems, pp. 1473–1480 (2005)Google Scholar
  11. 11.
    Xing, E., Ng, A., Jordan, M., Russell, S.: Distance metric learning, with application to clustering with side-information. In: Advances in Neural Information Processing Systems, vol. 15 (2003)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Catholic University of TemucoTemucoChile
  2. 2.Pontifical Catholic University of ChileSantiagoChile

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