A high performance CRF model for clothes parsing

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

*この研究の対応する著者

研究成果: Conference article査読

32 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)64-81
ページ数18
ジャーナルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9005
DOI
出版ステータスPublished - 2015
外部発表はい
イベント12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
継続期間: 2014 11月 12014 11月 5

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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