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Multiple-Shot Person Re-identification via Riemannian Discriminative Learning

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Abstract

This paper presents a Riemannian discriminative learning framework for multiple-shot person re-identification. Firstly, image regions are encoded into covariance matrices or a Gaussian extension as robust feature descriptors. Since these matrices lie on some specific Riemannian manifolds, we introduce a manifold averaging strategy to fuse the feature descriptors from multiple images for a holistic representation, and exploit Riemannian kernels to implicitly map the averaged matrices to a Reproducing Kernel Hilbert Space (RKHS), where conventional discriminative learning algorithms can be conducted. In particular, we apply kernel variants of two typical methods, i.e., the Linear Discriminant Analysis (LDA) and Metric Learning to Rank (MLR), to demonstrate the flexibility of the framework. Extensive experiments on five public datasets exhibit impressive improvements over existing multiple-shot re-identification methods as well as representative single-shot approaches.

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Notes

  1. 1.

    http://ivlab.sjtu.edu.cn/best.

  2. 2.

    The source code is released on our website: http://vipl.ict.ac.cn/resources/codes.

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Acknowledgement

This work is partially supported by 973 Program under contract No. 2015CB351802, Natural Science Foundation of China under contracts Nos. 61390511, 61379083, 61272321, 61271445, and Youth Innovation Promotion Association CAS No. 2015085.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, 100190, China

    Yuheng Lu, Ruiping Wang, Shiguang Shan & Xilin Chen

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yuheng Lu, Ruiping Wang, Shiguang Shan & Xilin Chen

  3. Cooperative Medianet Innovation Center, Beijing, China

    Ruiping Wang, Shiguang Shan & Xilin Chen

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  1. Yuheng Lu
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  2. Ruiping Wang
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  3. Shiguang Shan
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  4. Xilin Chen
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Correspondence to Ruiping Wang .

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Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University , Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

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Lu, Y., Wang, R., Shan, S., Chen, X. (2017). Multiple-Shot Person Re-identification via Riemannian Discriminative Learning. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10118. Springer, Cham. https://doi.org/10.1007/978-3-319-54526-4_30

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  • DOI: https://doi.org/10.1007/978-3-319-54526-4_30

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