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Chinese Conference on Pattern Recognition

CCPR 2016: Pattern Recognition pp 364–378Cite as

Attribute Based Approach for Clothing Recognition

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 663))

Abstract

Clothing recognition is hot topic for its potential benefits to lots of visual tasks, such as people identification, pose estimation and recommendation system. However, due to the wide variations of clothing appearance and the “semantic gap” between low-level features and high-level category concepts, clothing recognition is very challenging. To narrow this gap, a novel method, which uses intermediate attributes to bridge low-level features and high-level category labels, is proposed. This method first recognizes local attributes from low-level visual features, and then infers clothing category based on these attributes. To this end, DPM models and pixel-level parsing are applied to obtain geometric structure attributes, such as collar shape, and geometric size attributes, such as sleeve length, respectively. Then, Multiple Output Neural Networks are built to predict clothing category based on attributes. Experiments show that the performance of our method is superior to two state-of-the-art approaches on both of attribute and category recognition.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, China

    Wang Fan, Zhao Qiyang & Yin Baolin

  2. Intelligent Recognition and Image Processing Lab, Beihang University, Beijing, China

    Liu Qingjie

Authors
  1. Wang Fan
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  2. Zhao Qiyang
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  3. Liu Qingjie
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  4. Yin Baolin
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Corresponding author

Correspondence to Wang Fan .

Editor information

Editors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  2. Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China

    Xuelong Li

  3. Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China

    Xilin Chen

  4. Tsinghua University , Beijing, China

    Jie Zhou

  5. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  6. University of Electronic Science and Technology, Chengdu, Sichuan, China

    Hong Cheng

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© 2016 Springer Nature Singapore Pte Ltd.

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Fan, W., Qiyang, Z., Qingjie, L., Baolin, Y. (2016). Attribute Based Approach for Clothing Recognition. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_30

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  • DOI: https://doi.org/10.1007/978-981-10-3005-5_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3004-8

  • Online ISBN: 978-981-10-3005-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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