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Unsupervised Person Re-identification via Graph-Structured Image Matching

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Computer Vision – ACCV 2016 Workshops (ACCV 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10118))

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Abstract

This paper presents a novel unsupervised framework to solve both person re-identification and partial person re-identification problems. For each pedestrian image, we first use an established image segmentation method to generate superpixels to construct an Attributed Region Adjacency Graph (ARAG) in which nodes corresponding with superpixels and edges representing correlations between superpixels. We then apply region-based Normalized Cut to the graph to merge similar neighbouring superpixels to form natural image regions corresponding to various body parts and backgrounds. To tackle the occlusion problem often encountered in these applications, we apply Denoising Autoencoder with nonnegativity constrains to learn robust and part-based representation of image patches in each node of the graph. Finally, the similarity of an image pair is measured by the Earth Mover’s Distance (EMD) between the robust image signatures of the nodes in the corresponding ARAGs. We evaluate our methods on both person re-id and partial person re-id datasets and the results show that our new framework outperforms state of the art methods.

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Acknowledgments

This work is supported by Ningbo Science and Technology Bureau (Project No. 2012B10055 and 2013D10008) and by the International Doctoral Innovation Centre (IDIC) at the University of Nottingham Ningbo China.

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Authors and Affiliations

  1. The University of Nottingham, Ningbo, China

    Bolei Xu & Guoping Qiu

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  1. Bolei Xu
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  2. Guoping Qiu
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Correspondence to Bolei Xu .

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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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Xu, B., Qiu, G. (2017). Unsupervised Person Re-identification via Graph-Structured Image Matching. 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_23

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  • DOI: https://doi.org/10.1007/978-3-319-54526-4_23

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  • Online ISBN: 978-3-319-54526-4

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