Title :

Garment Fit Evaluation Using Machine Learning Technology

Author(s) :

Liu, Kaixuan [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Université de Lille
Xi'an Polytechnic University
École nationale supérieure des arts et industries textiles [ENSAIT]
Zeng, Xianyi [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
Université Lille Nord (France)
Bruniaux, Pascal [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
Tao, Xuyuan [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Université de Lille
École nationale supérieure des arts et industries textiles [ENSAIT]
Kamalha, Edwin [Auteur]
Génie et Matériaux Textiles [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Wang, Jianping [Auteur]
Donghua University [Shanghai]

Journal title :

Springer Series in Fashion Business

Abbreviated title :

Springer Series in Fashion Business

Volume number :

-

Pages :

273-288

Publication date :

2018-05-17

ISSN :

2366-8776

English keyword(s) :

Digital clothing pressure
Support vector machines
Naive Bayes Active learning
Ease allowance
Real try-on

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

Presently, garment fit evaluation mainly focuses on real try-on and rarely deals with virtual try-on. With the rapid development of e-commerce, there is a profound growth of garment purchases through the Internet. In this ...
Show more >Presently, garment fit evaluation mainly focuses on real try-on and rarely deals with virtual try-on. With the rapid development of e-commerce, there is a profound growth of garment purchases through the Internet. In this context, fit evaluation of virtual garment try-on is vital in the clothing industry. In this chapter, we propose a Naive Bayes-based model to evaluate garment fit. The inputs of the proposed model are digital clothing pressures of different body parts, generated from a 3D garment CAD software, while the output is the predicted result of garment fit (fit or unfit). To construct and train the proposed model, data on digital clothing pressures and garment real fit was collected for input and output learning data, respectively. By learning from these data, our proposed model can predict garment fit rapidly and automatically without any real try-on; therefore, it can be applied to remote garment fit evaluation in the context of e-shopping. Finally, the effectiveness of our proposed method was validated using a set of test samples. Test results showed that digital clothing pressure is a better index than ease allowance to evaluate garment fit, and machine learning-based garment fit evaluation methods have higher prediction accuracies.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

• Génie des matériaux textiles (GEMTEX) - ULR 2641

Submission date :

2023-06-20T10:59:10Z
2024-02-29T09:08:35Z