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Person Re-Identification Using Kernel-Based Metric Learning Methods

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 8695)

Abstract

Re-identification of individuals across camera networks with limited or no overlapping fields of view remains challenging in spite of significant research efforts. In this paper, we propose the use, and extensively evaluate the performance, of four alternatives for re-ID classification: regularized Pairwise Constrained Component Analysis, kernel Local Fisher Discriminant Analysis, Marginal Fisher Analysis and a ranking ensemble voting scheme, used in conjunction with different sizes of sets of histogram-based features and linear, χ 2 and RBF-χ 2 kernels. Comparisons against the state-of-art show significant improvements in performance measured both in terms of Cumulative Match Characteristic curves (CMC) and Proportion of Uncertainty Removed (PUR) scores on the challenging VIPeR, iLIDS, CAVIAR and 3DPeS datasets.

Keywords

  • Feature Vector
  • Ranking Algorithm
  • Camera Network
  • Hinge Loss
  • Scatter Matrice

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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Xiong, F., Gou, M., Camps, O., Sznaier, M. (2014). Person Re-Identification Using Kernel-Based Metric Learning Methods. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8695. Springer, Cham. https://doi.org/10.1007/978-3-319-10584-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-10584-0_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10583-3

  • Online ISBN: 978-3-319-10584-0

  • eBook Packages: Computer ScienceComputer Science (R0)