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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 pp 35–51Cite as

Pedestrian Color Naming via Convolutional Neural Network

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10112))

Abstract

Color serves as an important cue for many computer vision tasks. Nevertheless, obtaining accurate color description from images is non-trivial due to varying illumination conditions, view angles, and surface reflectance. This is especially true for the challenging problem of pedestrian description in public spaces. We made two contributions in this study: (1) We contribute a large-scale pedestrian color naming dataset with 14,213 hand-labeled images. (2) We address the problem of assigning consistent color name to regions of single object’s surface. We propose an end-to-end, pixel-to-pixel convolutional neural network (CNN) for pedestrian color naming. We demonstrate that our Pedestrian Color Naming CNN (PCN-CNN) is superior over existing approaches in providing consistent color names on real-world pedestrian images. In addition, we show the effectiveness of color descriptor extracted from PCN-CNN in complementing existing descriptors for the task of person re-identification. Moreover, we discuss a novel application to retrieve outfit matching and fashion (which could be difficult to be described by keywords) with just a user-provided color sketch.

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Notes

  1. 1.

    A basic color term is defined as being not subsumable to other basic color terms and extensively used in different languages.

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Acknowledgement

We would like to show our gratitude to the authors of [9], for sharing their features and codes of matching procedure for the person re-identification experiments.

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Authors and Affiliations

  1. Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China

    Zhiyi Cheng, Xiaoxiao Li & Chen Change Loy

Authors
  1. Zhiyi Cheng
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  2. Xiaoxiao Li
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  3. Chen Change Loy
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Corresponding author

Correspondence to Chen Change Loy .

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Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Cheng, Z., Li, X., Loy, C.C. (2017). Pedestrian Color Naming via Convolutional Neural Network. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10112. Springer, Cham. https://doi.org/10.1007/978-3-319-54184-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-54184-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54183-9

  • Online ISBN: 978-3-319-54184-6

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