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https://doi.org/10.1109/TMM.2013.2285526
Title: | Fashion parsing with weak color-category labels | Authors: | Liu, S. Feng, J. Domokos, C. Xu, H. Huang, J. Hu, Z. Yan, S. |
Keywords: | Fashion parsing Markov random fields weakly-supervised learning |
Issue Date: | Jan-2014 | Citation: | Liu, S., Feng, J., Domokos, C., Xu, H., Huang, J., Hu, Z., Yan, S. (2014-01). Fashion parsing with weak color-category labels. IEEE Transactions on Multimedia 16 (1) : 253-265. ScholarBank@NUS Repository. https://doi.org/10.1109/TMM.2013.2285526 | Abstract: | In this paper we address the problem of automatically parsing the fashion images with weak supervision from the user-generated color-category tags such as 'red jeans' and 'white T-shirt'. This problem is very challenging due to the large diversity of fashion items and the absence of pixel-level tags, which make the traditional fully supervised algorithms inapplicable. To solve the problem, we propose to combine the human pose estimation module, the MRF-based color and category inference module and the (super)pixel-level category classifier learning module to generate multiple well-performing category classifiers, which can be directly applied to parse the fashion items in the images. Besides, all the training images are parsed with color-category labels and the human poses of the images are estimated during the model learning phase in this work. We also construct a new fashion image dataset called Colorful-Fashion, in which all 2,682 images are labeled with pixel-level color-category labels. Extensive experiments on this dataset clearly show the effectiveness of the proposed method for the weakly supervised fashion parsing task. © 1999-2012 IEEE. | Source Title: | IEEE Transactions on Multimedia | URI: | http://scholarbank.nus.edu.sg/handle/10635/56004 | ISSN: | 15209210 | DOI: | 10.1109/TMM.2013.2285526 |
Appears in Collections: | Staff Publications |
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