Please use this identifier to cite or link to this item:
https://doi.org/10.1109/TMM.2013.2285526
DC Field | Value | |
---|---|---|
dc.title | Fashion parsing with weak color-category labels | |
dc.contributor.author | Liu, S. | |
dc.contributor.author | Feng, J. | |
dc.contributor.author | Domokos, C. | |
dc.contributor.author | Xu, H. | |
dc.contributor.author | Huang, J. | |
dc.contributor.author | Hu, Z. | |
dc.contributor.author | Yan, S. | |
dc.date.accessioned | 2014-06-17T02:49:38Z | |
dc.date.available | 2014-06-17T02:49:38Z | |
dc.date.issued | 2014-01 | |
dc.identifier.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 | |
dc.identifier.issn | 15209210 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/56004 | |
dc.description.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. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TMM.2013.2285526 | |
dc.source | Scopus | |
dc.subject | Fashion parsing | |
dc.subject | Markov random fields | |
dc.subject | weakly-supervised learning | |
dc.type | Article | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.contributor.department | ELECTRICAL & COMPUTER ENGINEERING | |
dc.description.doi | 10.1109/TMM.2013.2285526 | |
dc.description.sourcetitle | IEEE Transactions on Multimedia | |
dc.description.volume | 16 | |
dc.description.issue | 1 | |
dc.description.page | 253-265 | |
dc.description.coden | ITMUF | |
dc.identifier.isiut | 000328948100021 | |
Appears in Collections: | Staff Publications |
Show simple item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.