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Fashion Outfit Style Retrieval Based on Hashing Method

  • Yujuan Ding
  • Wai Keung Wong
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)

Abstract

This paper proposes an outfit retrieval method for moving fashion items dressed by models in the catwalk videos. The proposed method aims at retrieving similar style in fashion outfits images using a bilinear supervised hashing algorithm. The targeted images are labeled with the information of both color and attributes for comprehensive description of clothing style. The speed up robust features (SURFs) are extracted as the low-level features of fashion images and fed into the hashing algorithm as original data. To achieve better retrieval performance, a bilinear supervised hashing method is employed to learn high-quality hash codes. Outfits with similar style to the target are expected to be retrieved by hash code ranking. The experiment was conducted on a dataset composed of fashion outfit images and the experimental results show that the proposed method can retrieve styles which are close to query of color and attributes.

Keywords

Fashion style retrieval Fashion catwalk images Hashing algorithm 

Notes

Acknowledgements

This paper was supported by the Hong Kong Polytechnic University.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Textiles and ClothingThe Hong Kong Polytechnic UniversityHong KongHong Kong

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