{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:19:23Z","timestamp":1763201963938,"version":"3.41.0"},"reference-count":30,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2016,3,8]],"date-time":"2016-03-08T00:00:00Z","timestamp":1457395200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2016,6,15]]},"abstract":"<jats:p>Previous image parsing methods usually model the problem in a conditional random field which describes a statistical model learned from a training dataset and then processes a query image using the conditional probability. However, for clothing images, fashion items have a large variety of layering and configuration, and it is hard to learn a certain statistical model of features that apply to general cases. In this article, we take fashion images as an example to show how Markov Random Fields (MRFs) can outperform Conditional Random Fields when the application does not follow a certain statistical model learned from the training data set. We propose a new method for automatically parsing fashion images in high processing efficiency with significantly less training time by applying a modification of MRFs, named reweighted MRF (RW-MRF), which resolves the problem of over smoothing infrequent labels. We further enhance RW-MRF with occlusion prior and background prior to resolve two other common problems in clothing parsing, occlusion, and background spill. Our experimental results indicate that our proposed clothing parsing method significantly improves processing time and training time over state-of-the-art methods, while ensuring comparable parsing accuracy and improving label recall rate.<\/jats:p>","DOI":"10.1145\/2890104","type":"journal-article","created":{"date-parts":[[2016,3,14]],"date-time":"2016-03-14T15:33:46Z","timestamp":1457969626000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Enhanced Reweighted MRFs for Efficient Fashion Image Parsing"],"prefix":"10.1145","volume":"12","author":[{"given":"Qiong","family":"Wu","sequence":"first","affiliation":[{"name":"University of Alberta, Edmonton, Alberta"}]},{"given":"Pierre","family":"Boulanger","sequence":"additional","affiliation":[{"name":"University of Alberta, Edmonton, Alberta"}]}],"member":"320","published-online":{"date-parts":[[2016,3,8]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2004.60"},{"key":"e_1_2_2_4_1","doi-asserted-by":"crossref","unstructured":"J. Deng W. Dong R. Socher L.-J. Li K. Li and L. Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In CVPR09.  J. Deng W. Dong R. Socher L.-J. Li K. Li and L. Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In CVPR09.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.113"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.423"},{"volume-title":"LIBLINEAR: A library for large linear classification. J. Mach. Learn. 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In Computer Vision\u2014ECCV","year":"2010","author":"Tighe Joseph","key":"e_1_2_2_21_1"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-005-6642-x"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33712-3_3"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2005.148"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISM.2014.75"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.437"},{"volume-title":"Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR\u201912)","author":"Yamaguchi Kota","key":"e_1_2_2_27_1"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.407"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995741"},{"key":"e_1_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Shuai Zheng Sadeep Jayasumana Bernardino Romera-Paredes Vibhav Vineet Zhizhong Su Dalong Du Chang Huang and Philip Torr. 2015. Conditional random fields as recurrent neural networks. arXiv:1502.03240 (2015).  Shuai Zheng Sadeep Jayasumana Bernardino Romera-Paredes Vibhav Vineet Zhizhong Su Dalong Du Chang Huang and Philip Torr. 2015. Conditional random fields as recurrent neural networks. arXiv:1502.03240 (2015).","DOI":"10.1109\/ICCV.2015.179"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2890104","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2890104","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:38:55Z","timestamp":1750221535000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2890104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,8]]},"references-count":30,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2016,6,15]]}},"alternative-id":["10.1145\/2890104"],"URL":"https:\/\/doi.org\/10.1145\/2890104","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"type":"print","value":"1551-6857"},{"type":"electronic","value":"1551-6865"}],"subject":[],"published":{"date-parts":[[2016,3,8]]},"assertion":[{"value":"2015-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2015-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2016-03-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}