TY - JOUR
T1 - Dress with Style
T2 - Learning Style from Joint Deep Embedding of Clothing Styles and Body Shapes
AU - Hidayati, Shintami Chusnul
AU - Goh, Ting Wei
AU - Chan, Ji Sheng Gary
AU - Hsu, Cheng Chun
AU - See, John
AU - Wong, Lai Kuan
AU - Hua, Kai Lung
AU - Tsao, Yu
AU - Cheng, Wen Huang
N1 - Funding Information:
Manuscript received August 8, 2019; revised January 24, 2020; accepted February 25, 2020. Date of publication March 12, 2020; date of current version December 17, 2020. This work was supported in part by the Ministry of Science and Technology of Taiwan under Grants MOST-108-2218-E-009-056, MOST-108-2745-8-009-002, MOST-108-2218-E-002-055, and MOST-109-2634-F-007-013. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yongdong Zhang. (Corresponding author: Wen-Huang Cheng.) Shintami Chusnul Hidayati is with the Department of Informatics, In-stitut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia (e-mail: [email protected]).
Publisher Copyright:
© 1999-2012 IEEE.
PY - 2020/3/12
Y1 - 2020/3/12
N2 - 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.
AB - 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.
KW - clothing style
KW - correlation
KW - Fashion analysis
KW - human body shape
KW - recommender system
UR - http://www.scopus.com/inward/record.url?scp=85098183034&partnerID=8YFLogxK
U2 - 10.1109/TMM.2020.2980195
DO - 10.1109/TMM.2020.2980195
M3 - Article
AN - SCOPUS:85098183034
SN - 1520-9210
VL - 23
SP - 365
EP - 377
JO - IEEE Transactions on Multimedia
JF - IEEE Transactions on Multimedia
ER -