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Local Descriptors Encoded by Fisher Vectors for Person Re-identification

  • Bingpeng Ma
  • Yu Su
  • Frédéric Jurie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)

Abstract

This paper proposes a new descriptor for person re-identification building on the recent advances of Fisher Vectors. Specifically, a simple vector of attributes consisting in the pixel coordinates, its intensity as well as the first and second-order derivatives is computed for each pixel of the image. These local descriptors are turned into Fisher Vectors before being pooled to produce a global representation of the image. The so-obtained Local Descriptors encoded by Fisher Vector (LDFV) have been validated through experiments on two person re-identification benchmarks (VIPeR and ETHZ), achieving state-of-the-art performance on both datasets.

Keywords

Gaussian Mixture Model Local Binary Pattern IEEE Conf Local Descriptor Pairwise Constraint 
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 2012

Authors and Affiliations

  • Bingpeng Ma
    • 1
  • Yu Su
    • 1
  • Frédéric Jurie
    • 1
  1. 1.GREYC — CNRS UMR 6072University of Caen Basse-NormandieCaenFrance

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