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Look Closely: Learning Exemplar Patches for Recognizing Textiles from Product Images

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Computer Vision – ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9003))

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Abstract

The resolution of product images is becoming higher dues to the rapid development of digital cameras and the Internet. Higher resolution images expose novel feature relationships that did not exist before. For instance, from a large image of a garment, one can observe the overall shape, the wrinkles, and the micro-level details such as sewing lines and weaving patterns. The key idea of our work is to combine features obtained at such largely different scales to improve textile recognition performance. Specifically, we develop a robust semi-supervised model that exploits both micro textures and macro deformable shapes to select representative patches from product images. The selected patches are then used as inputs to conventional texture recognition methods to perform texture recognition. We show that, by learning from human-provided image regions, the method can suggest more discriminative regions that lead to higher categorization rates (+5-7 %). We also show that our patch selection method significantly improves the performance of conventional texture recognition methods that usually rely on dense sampling. Our dataset of labeled textile images will be released for further investigation in this emerging field.

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Notes

  1. 1.

    We use the term “recognition” to refer to both patch selection and categorization.

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Author information

Authors and Affiliations

  1. School of Creative Media, City University of Hong Kong, Kowloon, Hong Kong

    Quoc Huy Phan & Hongbo Fu

  2. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Antoni B. Chan

Authors
  1. Quoc Huy Phan
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  2. Hongbo Fu
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  3. Antoni B. Chan
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Corresponding author

Correspondence to Quoc Huy Phan .

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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Phan, Q.H., Fu, H., Chan, A.B. (2015). Look Closely: Learning Exemplar Patches for Recognizing Textiles from Product Images. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_30

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  • DOI: https://doi.org/10.1007/978-3-319-16865-4_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16864-7

  • Online ISBN: 978-3-319-16865-4

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