Paper doll parsing: Retrieving similar styles to parse clothing items

Kota Yamaguchi, M. Hadi Kiapour, Tamara L. Berg

Research output: Chapter in Book/Report/Conference proceedingConference contribution

161 Citations (Scopus)

Abstract

Clothing recognition is an extremely challenging problem due to wide variation in clothing item appearance, layering, and style. In this paper, we tackle the clothing parsing problem using a retrieval based approach. For a query image, we find similar styles from a large database of tagged fashion images and use these examples to parse the query. Our approach combines parsing from: pre-trained global clothing models, local clothing models learned on the fly from retrieved examples, and transferred parse masks (paper doll item transfer) from retrieved examples. Experimental evaluation shows that our approach significantly outperforms state of the art in parsing accuracy.

Original languageEnglish
Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3519-3526
Number of pages8
ISBN (Print)9781479928392
DOIs
Publication statusPublished - 2013 Jan 1
Event2013 14th IEEE International Conference on Computer Vision, ICCV 2013 - Sydney, NSW, Australia
Duration: 2013 Dec 12013 Dec 8

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Other

Other2013 14th IEEE International Conference on Computer Vision, ICCV 2013
CountryAustralia
CitySydney, NSW
Period13/12/113/12/8

Keywords

  • clothing parsing
  • clothing recognition
  • segmentation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Yamaguchi, K., Kiapour, M. H., & Berg, T. L. (2013). Paper doll parsing: Retrieving similar styles to parse clothing items. In Proceedings - 2013 IEEE International Conference on Computer Vision, ICCV 2013 (pp. 3519-3526). [6751549] (Proceedings of the IEEE International Conference on Computer Vision). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2013.437