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Article: Clothes Co-Parsing Via Joint Image Segmentation and Labeling with Application to Clothing Retrieval

TitleClothes Co-Parsing Via Joint Image Segmentation and Labeling with Application to Clothing Retrieval
Authors
Issue Date2016
Citation
IEEE Transactions on Multimedia, 2016, v. 18, n. 6, p. 1175-1186 How to Cite?
Abstract© 1999-2012 IEEE. This paper aims at developing an integrated system for clothing co-parsing (CCP), in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. A novel data-driven system consisting of two phases of inference is proposed. The first phase, referred as 'image cosegmentation,' iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM technique [1]. In the second phase (i.e., 'region colabeling'), we construct a multiimage graphical model by taking the segmented regions as vertices, and incorporating several contexts of clothing configuration (e.g., item locations and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [2], we construct a dataset called the SYSU-Clothes dataset consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29%/88.23% segmentation accuracy and 65.52%/63.89% recognition rate on the Fashionista and the SYSU-Clothes datasets, respectively, which are superior compared with the previous methods. Furthermore, we apply our method on a challenging task, i.e., cross-domain clothing retrieval: given user photo depicting a clothing image, retrieving the same clothing items from online shopping stores based on the fine-grained parsing results.
Persistent Identifierhttp://hdl.handle.net/10722/273561
ISSN
2023 Impact Factor: 8.4
2023 SCImago Journal Rankings: 2.260
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiang, Xiaodan-
dc.contributor.authorLin, Liang-
dc.contributor.authorYang, Wei-
dc.contributor.authorLuo, Ping-
dc.contributor.authorHuang, Junshi-
dc.contributor.authorYan, Shuicheng-
dc.date.accessioned2019-08-12T09:55:56Z-
dc.date.available2019-08-12T09:55:56Z-
dc.date.issued2016-
dc.identifier.citationIEEE Transactions on Multimedia, 2016, v. 18, n. 6, p. 1175-1186-
dc.identifier.issn1520-9210-
dc.identifier.urihttp://hdl.handle.net/10722/273561-
dc.description.abstract© 1999-2012 IEEE. This paper aims at developing an integrated system for clothing co-parsing (CCP), in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. A novel data-driven system consisting of two phases of inference is proposed. The first phase, referred as 'image cosegmentation,' iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM technique [1]. In the second phase (i.e., 'region colabeling'), we construct a multiimage graphical model by taking the segmented regions as vertices, and incorporating several contexts of clothing configuration (e.g., item locations and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [2], we construct a dataset called the SYSU-Clothes dataset consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29%/88.23% segmentation accuracy and 65.52%/63.89% recognition rate on the Fashionista and the SYSU-Clothes datasets, respectively, which are superior compared with the previous methods. Furthermore, we apply our method on a challenging task, i.e., cross-domain clothing retrieval: given user photo depicting a clothing image, retrieving the same clothing items from online shopping stores based on the fine-grained parsing results.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Multimedia-
dc.titleClothes Co-Parsing Via Joint Image Segmentation and Labeling with Application to Clothing Retrieval-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TMM.2016.2542983-
dc.identifier.scopuseid_2-s2.0-84971234020-
dc.identifier.volume18-
dc.identifier.issue6-
dc.identifier.spage1175-
dc.identifier.epage1186-
dc.identifier.isiWOS:000376107100019-
dc.identifier.issnl1520-9210-

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