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Visual Attributes for Fashion Analytics

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Visual Attributes

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

In this chapter, we describe methods that leverage clothing and facial attributes as mid-level features for fashion recommendation and retrieval. We introduce a system called Magic Closet for recommending clothing for different occasions, and a system called Beauty E-Expert for hairstyle and facial makeup recommendation. For fashion retrieval, we describe a cross-domain clothing retrieval system, which receives as input a user photo of a particular clothing item taken in unconstrained conditions, and retrieves the exact same or similar item from online shopping catalogs. In each of these systems, we show the value of attribute-guided learning and describe approaches to transfer semantic concepts from large-scale uncluttered annotated data to challenging real-world imagery.

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Notes

  1. 1.

    http://www.dresscodeguide.com/.

  2. 2.

    Dress codes are written and unwritten rules with regards to clothing.

  3. 3.

    Although the clothes of a user can be changed to make one look more beautiful, they are kept fixed in our current Beauty e-Experts system.

  4. 4.

    http://www.di.ens.fr/~mschmidt/Software/UGM.html.

  5. 5.

    http://www.realbeauty.com/hair/virtual/hairstyles.

  6. 6.

    http://www.dailymakeover.com/games-apps/games.

  7. 7.

    http://www.taaz.com.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Si Liu

  2. IBM T. J. Watson Research Center, New York, England

    Lisa M. Brown

  3. Qihoo 360 Artificial Intelligence Institute, Beijing, China

    Qiang Chen, Junshi Huang, Luoqi Liu & Shuicheng Yan

Authors
  1. Si Liu
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  2. Lisa M. Brown
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  3. Qiang Chen
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  4. Junshi Huang
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  5. Luoqi Liu
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  6. Shuicheng Yan
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Corresponding author

Correspondence to Si Liu .

Editor information

Editors and Affiliations

  1. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Schmidt Feris

  2. IST Austria Computer Vision and Machine Learning, Klosterneuburg, Austria

    Christoph Lampert

  3. Virginia Tech Electrical and Computer Engineering, Blacksburg, Virginia, USA

    Devi Parikh

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Liu, S., Brown, L.M., Chen, Q., Huang, J., Liu, L., Yan, S. (2017). Visual Attributes for Fashion Analytics. In: Feris, R., Lampert, C., Parikh, D. (eds) Visual Attributes. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-50077-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-50077-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50075-1

  • Online ISBN: 978-3-319-50077-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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