KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Fashion Parsing With Weak Color-Category Labels | |
Liu, Si1 ; Feng, Jiashi1; Domokos, Csaba1; Xu, Hui2 ; Huang, Junshi1; Hu, Zhenzhen3; Yan, Shuicheng1
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| 2014 | |
| 摘要 | 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. |
| 关键词 | Fashion parsing Markov random fields weakly-supervised learning |
| DOI | 10.1109/TMM.2013.2285526 |
| 发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA
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| ISSN | 1520-9210 |
| 卷号 | 16期号:1页码:253-265 |
| 通讯作者 | Liu, S (reprint author), Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117548, Singapore. |
| 收录类别 | SCI |
| WOS记录号 | WOS:000328948100021 |
| 语种 | 英语 |
