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A High Performance CRF Model for Clothes Parsing

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Book cover Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9005))

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Abstract

In this paper we tackle the problem of clothing parsing: Our goal is to segment and classify different garments a person is wearing. We frame the problem as the one of inference in a pose-aware Conditional Random Field (CRF) which exploits appearance, figure/ground segmentation, shape and location priors for each garment as well as similarities between segments, and symmetries between different human body parts. We demonstrate the effectiveness of our approach on the Fashionista dataset [1] and show that we can obtain a significant improvement over the state-of-the-art.

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Acknowledgements

This work has been partially funded by Spanish Ministry of Economy and Competitiveness under projects PAU+ DPI2011-27510 and ERA-Net Chistera project ViSen PCIN-2013-047.

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Authors and Affiliations

  1. IRI (CSIC-UPC), Barcelona, Spain

    Edgar Simo-Serra & Francesc Moreno-Noguer

  2. University of Toronto, Toronto, Canada

    Sanja Fidler & Raquel Urtasun

Authors
  1. Edgar Simo-Serra
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  2. Sanja Fidler
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  3. Francesc Moreno-Noguer
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  4. Raquel Urtasun
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Corresponding author

Correspondence to Edgar Simo-Serra .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Simo-Serra, E., Fidler, S., Moreno-Noguer, F., Urtasun, R. (2015). A High Performance CRF Model for Clothes Parsing. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-16811-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16810-4

  • Online ISBN: 978-3-319-16811-1

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