https://scholars.lib.ntu.edu.tw/handle/123456789/628558
標題: | Dress With Style: Learning Style From Joint Deep Embedding of Clothing Styles and Body Shapes | 作者: | Hidayati, SC Goh, TW Chan, JSG Hsu, CC See, J Wong, LK Hua, KL Tsao, Y WEN-HUANG CHENG |
關鍵字: | Clothing; Shape; Correlation; Shape measurement; Task analysis; Big Data; Semantics; Fashion analysis; recommender system; human body shape; clothing style; correlation; RETRIEVAL; COLOR | 公開日期: | 2021 | 出版社: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | 卷: | 23 | 起(迄)頁: | 365 | 來源出版物: | IEEE TRANSACTIONS ON MULTIMEDIA | 摘要: | Body shape is about proportion, and fashion style is all about dressing those proportions to look their very best. Figuring out the styles to suit a body shape can be a daunting task for many people. It is, therefore, essential to develop a framework for learning the compatibility of body shapes and clothing styles. Though fashion designers and fashion stylists have analyzed the correlation between human body shapes and fashion styles for a long time, this issue did not receive much attention in multimedia science. In this paper, we present a novel style recommender, on the basis of the user's body attributes. The rich amount of fashion styling knowledge from social big data is exploited for this purpose. We first construct a joint embedding of clothing styles and human body measurements with deep multimodal representation learning on a reference dataset that has been sorted to meet the fashion rules. We then discover the relevant semantic features by propagation and selection in clothing style and body shape graphs. Experiments demonstrate the effectiveness of the proposed framework when compared with several baseline methods. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/628558 | ISSN: | 1520-9210 | DOI: | 10.1109/TMM.2020.2980195 |
顯示於: | 資訊工程學系 |
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