A high performance CRF model for clothes parsing

Edgar Simo-Serra*, Sanja Fidler, Francesc Moreno-Noguer, Raquel Urtasun

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

37 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)64-81
Number of pages18
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 2015
Externally publishedYes
Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
Duration: 2014 Nov 12014 Nov 5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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