Learning Pairwise Dissimilarity Profiles for Appearance Recognition in Visual Surveillance

  • Authors:
  • Zhe Lin;Larry S. Davis

  • Affiliations:
  • Institute of Advanced Computer Studies, University of Maryland, College Park, MD 20742;Institute of Advanced Computer Studies, University of Maryland, College Park, MD 20742

  • Venue:
  • ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

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.