A high performance CRF model for clothes parsing

Conference Article


Asian Conference on Computer Vision (ACCV)





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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.


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.