A high performance CRF model for clothes parsing

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

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

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)
Volume9005
DOIs
Publication statusPublished - 2015
Externally publishedYes

Fingerprint

Conditional Random Fields
Parsing
High Performance
Figure
Person
Segmentation
Classify
Symmetry
Model
Demonstrate
Similarity
Human

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

A high performance CRF model for clothes parsing. / Simo Serra, Edgar; Fidler, Sanja; Moreno-Noguer, Francesc; Urtasun, Raquel.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9005, 2015, p. 64-81.

Research output: Contribution to journalArticle

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