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Mahalanobis Distance Learning for Person Re-identification

  • Peter M. Roth
  • Martin Hirzer
  • Martin Köstinger
  • Csaba Beleznai
  • Horst Bischof
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Recently, Mahalanobis metric learning has gained a considerable interest for single-shot person re-identification. The main idea is to build on an existing image representation and to learn a metric that reflects the visual camera-to-camera transitions, allowing for a more powerful classification. The goal of this chapter is twofold. We first review the main ideas of Mahalanobis metric learning in general and then give a detailed study on different approaches for the task of single-shot person re-identification, also comparing to the state of the art. In particular, for our experiments, we used Linear Discriminant Metric Learning (LDML), Information Theoretic Metric Learning (ITML), Large Margin Nearest Neighbor (LMNN), Large Margin Nearest Neighbor with Rejection (LMNN-R), Efficient Impostor-based Metric Learning (EIML), and KISSME. For our evaluations we used four different publicly available datasets (i.e., VIPeR, ETHZ, PRID 2011, and CAVIAR4REID). Additionally, we generated the new, more realistic PRID 450S dataset, where we also provide detailed segmentations. For the latter one, we also evaluated the influence of using well-segmented foreground and background regions. Finally, the corresponding results are presented and discussed.

Keywords

Linear Discriminant Analysis Image Pair Local Binary Pattern Mahalanobis Distance Camera View 
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 London 2014

Authors and Affiliations

  • Peter M. Roth
    • 1
  • Martin Hirzer
    • 1
  • Martin Köstinger
    • 1
  • Csaba Beleznai
    • 2
  • Horst Bischof
    • 1
  1. 1.Graz University of TechnologyGrazAustria
  2. 2.Austrian Institute of TechnologyViennaAustria

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