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dc.contributor.authorHidayati, Shintami C.en_US
dc.contributor.authorHua, Kai-Lungen_US
dc.contributor.authorTsao, Yuen_US
dc.contributor.authorShuai, Hong-Hanen_US
dc.contributor.authorLiu, Jiayingen_US
dc.contributor.authorCheng, Wen-Huangen_US
dc.date.accessioned2019-12-13T01:12:52Z-
dc.date.available2019-12-13T01:12:52Z-
dc.date.issued2019-01-01en_US
dc.identifier.isbn978-1-7281-1198-8en_US
dc.identifier.urihttp://dx.doi.org/10.1109/MIPR.2019.00095en_US
dc.identifier.urihttp://hdl.handle.net/11536/153292-
dc.description.abstractClothing image analysis has shown its potential for use in a wide range of applications such as personalized clothing recommendation. Given a consumer photo, this paper addresses the problem of finding clothes and recognizing the genre of that clothes. This problem is very challenging due to large variations of uncontrolled realistic imaging conditions. To tackle these challenges, we formulate a novel framework by integrating local features of multi-modality as the instances of the price-collecting Steiner tree (PCST) problem to discover clothing regions, and exploiting visual style elements to discover the clothing genre. The experimental results show that our fully automatic approach is effective to identify irregular shape of clothing region, and it significantly improves the accuracy of clothing genre recognition for images taken in unconstrained environment.en_US
dc.language.isoen_USen_US
dc.titleGarment Detectives: Discovering Clothes and Its Genre in Consumer Photosen_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/MIPR.2019.00095en_US
dc.identifier.journal2019 2ND IEEE CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2019)en_US
dc.citation.spage471en_US
dc.citation.epage474en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.department電機工程學系zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.contributor.departmentDepartment of Electrical and Computer Engineeringen_US
dc.identifier.wosnumberWOS:000493730600087en_US
dc.citation.woscount0en_US
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