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Evaluating Feature Importance for Re-identification

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Book cover Person Re-Identification

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

Person re-identification methods seek robust person matching through combining feature types. Often, these features are assigned implicitly with a single vector of global weights, which are assumed to be universally and equally good for matching all individuals, independent of their different appearances. In this study, we present a comprehensive comparison and evaluation of up-to-date imagery features for person re-identification. We show that certain features play more important roles than others for different people. To that end, we introduce an unsupervised approach to learning a bottom-up measurement of feature importance. This is achieved through first automatically grouping individuals with similar appearance characteristics into different prototypes/clusters. Different features extracted from different individuals are then automatically weighted adaptively driven by their inherent appearance characteristics defined by the associated prototype. We show comparative evaluation on the re-identification effectiveness of the proposed prototype-sensitive feature importance-based method as compared to two generic weight-based global feature importance methods. We conclude by showing that their combination is able to yield more accurate person re-identification.

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Notes

  1. 1.

    Since HSV and YCbCr share similar luminance/brightness channel, dropping one of them results in a total of 8 channels.

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Author information

Authors and Affiliations

  1. Tsinghua University, Beijing, China

    Chunxiao Liu & Xinggang Lin

  2. Queen Mary University of London, London, UK

    Shaogang Gong

  3. The Chinese University of Hong Kong, Hong Kong, China

    Chen Change Loy

Authors
  1. Chunxiao Liu
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  2. Shaogang Gong
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  3. Chen Change Loy
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  4. Xinggang Lin
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Corresponding author

Correspondence to Chunxiao Liu .

Editor information

Editors and Affiliations

  1. Queen Mary University, London, United Kingdom

    Shaogang Gong

  2. University of Verona, Italy

    Marco Cristani

  3. National University of Singapore, Singapore

    Shuicheng Yan

  4. The Chinese University of Hong Kong, Shatin, Hong Kong SAR

    Chen Change Loy

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© 2014 Springer-Verlag London

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Liu, C., Gong, S., Loy, C.C., Lin, X. (2014). Evaluating Feature Importance for Re-identification. In: Gong, S., Cristani, M., Yan, S., Loy, C. (eds) Person Re-Identification. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6296-4_10

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  • DOI: https://doi.org/10.1007/978-1-4471-6296-4_10

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6295-7

  • Online ISBN: 978-1-4471-6296-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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