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Content Based Image Search for Clothing Recommendations in E-Commerce

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Multimedia Data Mining and Analytics

Abstract

A number of algorithms exist in measuring clothing similarity for clothing recommendations in E-commerce. The clothing similarity mostly depends on its shape, texture and style. In this paper we introduce three models of defining feature space for clothing recommendations. The sketch-based image search mainly focuses on defining similarity of clothing in contour dimension. The spatial bag-of-feature approach is employed to measure the clothing similarity of local image patterns. Finally, we introduce a query adaptive shape model which combines shape characteristics and labels of clothing, in order to take the semantic information of clothing. A large number of simulations are given to show the feasibility and performance of the clothing recommendations by using content-based image search.

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Acknowledgments

The work was supported by the national natural science foundation of China (Grant Nos. 91120305, 61272251).

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Authors and Affiliations

  1. Brain-Like Computing and Machine Intelligence Lab, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, Shanghai, 200240, China

    Haoran Wang, Zhengzhong Zhou, Changcheng Xiao & Liqing Zhang

Authors
  1. Haoran Wang
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  2. Zhengzhong Zhou
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  3. Changcheng Xiao
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  4. Liqing Zhang
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Corresponding author

Correspondence to Liqing Zhang .

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Editors and Affiliations

  1. IBM Corp., Durham, North Carolina, USA

    Aaron K. Baughman

  2. Nokia Inc., Sunnyvale, California, USA

    Jiang Gao

  3. Google Inc., Mountain View, California, USA

    Jia-Yu Pan

  4. 4i, Inc., Carlsbad, California, USA

    Valery A. Petrushin

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Wang, H., Zhou, Z., Xiao, C., Zhang, L. (2015). Content Based Image Search for Clothing Recommendations in E-Commerce. In: Baughman, A., Gao, J., Pan, JY., Petrushin, V. (eds) Multimedia Data Mining and Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-14998-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-14998-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14997-4

  • Online ISBN: 978-3-319-14998-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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