Retrieving similar styles to parse clothing

Kota Yamaguchi, M. Hadi Kiapour, Luis E. Ortiz, Tamara L. Berg

Research output: Contribution to journalArticlepeer-review

86 Citations (Scopus)

Abstract

Clothing recognition is a societally and commercially important yet extremely challenging problem due to large variations in clothing appearance, layering, style, and body shape and pose. 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 recognize clothing items in 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. We evaluate our approach extensively and show significant improvements over previous state-of-the-art for both localization (clothing parsing given weak supervision in the form of tags) and detection (general clothing parsing). Our experimental results also indicate that the general pose estimation problem can benefit from clothing parsing.

Original languageEnglish
Article number6888484
Pages (from-to)1028-1040
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume37
Issue number5
DOIs
Publication statusPublished - 2015 May 1

Keywords

  • Clothing parsing
  • clothing recognition
  • image parsing
  • pose estimation
  • semantic segmentation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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