Person Re-identification by Descriptive and Discriminative Classification

  • Martin Hirzer
  • Csaba Beleznai
  • Peter M. Roth
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6688)


Person re-identification, i.e., recognizing a single person across spatially disjoint cameras, is an important task in visual surveillance. Existing approaches either try to find a suitable description of the appearance or learn a discriminative model. Since these different representational strategies capture a large extent of complementary information we propose to combine both approaches. First, given a specific query, we rank all samples according to a feature-based similarity, where appearance is modeled by a set of region covariance descriptors. Next, a discriminative model is learned using boosting for feature selection, which provides a more specific classifier. The proposed approach is demonstrated on two datasets, where we show that the combination of a generic descriptive statistical model and a discriminatively learned feature-based model attains considerably better results than the individual models alone. In addition, we give a comparison to the state-of-the-art on a publicly available benchmark dataset.


Feature Selection Image Pair Probe Image Discriminative Model Covariance Feature 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Martin Hirzer
    • 1
  • Csaba Beleznai
    • 2
  • Peter M. Roth
    • 1
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics and VisionGraz University of TechnologyAustria
  2. 2.Austrian Institute of TechnologyAustria

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