Publication
A high performance CRF model for clothes parsing
Conference Article
Conference
Asian Conference on Computer Vision (ACCV)
Edition
12th
Pages
64-81
Doc link
http://dx.doi.org/10.1007/978-3-319-16811-1_5
File
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 and show that we can obtain a significant improvement over the state-of-the-art.
Categories
computer vision, image recognition, pattern recognition.
Author keywords
computer vision, image segmentation, machine learning, conditional random fields
Scientific reference
E. Simo-Serra, S. Fidler, F. Moreno-Noguer and R. Urtasun. A high performance CRF model for clothes parsing, 12th Asian Conference on Computer Vision, 2014, Singapore, in Computer Vision - ACCV 2014, Vol 9005 of Lecture Notes in Computer Science, pp. 64-81, 2015, Springer.
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