Research Article
Clothing Style Recognition using Fashion Attribute Detection
@ARTICLE{10.4108/icst.mobimedia.2015.259089, author={Guang-Lu Sun and Xiao Wu and Hong-Han Chen and Qiang Peng}, title={Clothing Style Recognition using Fashion Attribute Detection}, journal={EAI Endorsed Transactions on Ambient Systems}, volume={2}, number={5}, publisher={EAI}, journal_a={AMSYS}, year={2015}, month={8}, keywords={clothing style, fashion, attribute}, doi={10.4108/icst.mobimedia.2015.259089} }- Guang-Lu Sun
Xiao Wu
Hong-Han Chen
Qiang Peng
Year: 2015
Clothing Style Recognition using Fashion Attribute Detection
AMSYS
EAI
DOI: 10.4108/icst.mobimedia.2015.259089
Abstract
In this paper, a new framework is proposed for clothing style recognition in natural scenes. Clothing region is first detected through the fusion of super-pixel segmentation, saliency detection and Gaussian Mixture Model (GMM). Next, a group of fashion attribute detectors are trained to get the likelihood of each attribute in the clothing image. Finally, the correlation matrix between clothing styles and fashion attributes is adopted to predict the clothing style. For evaluation, we collect a dataset for clothing style recognition which contains 5 styles and 14 fashion attributes. Extensive experiments demonstrate that the proposed framework has a promising ability to recognize the clothing style.
Copyright © 2015 G-L. Sun et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
