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Learning Pairwise Dissimilarity Profiles for Appearance Recognition in Visual Surveillance

  • Zhe Lin
  • Larry S. Davis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5358)

Abstract

Training discriminative classifiers for a large number of classes is a challenging problem due to increased ambiguities between classes. In order to better handle the ambiguities and to improve the scalability of classifiers to larger number of categories, we learn pairwise dissimilarity profiles (functions of spatial location) between categories and adapt them into nearest neighbor classification. We introduce a dissimilarity distance measure and linearly or nonlinearly combine it with direct distances. We illustrate and demonstrate the approach mainly in the context of appearance-based person recognition.

Keywords

Recognition Rate Near Neighbor Visual Surveillance Person Recognition Pairwise Dissimilarity 
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 2008

Authors and Affiliations

  • Zhe Lin
    • 1
  • Larry S. Davis
    • 1
  1. 1.Institute of Advanced Computer StudiesUniversity of MarylandCollege Park

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