A novel evaluation technique for human body perception of clothing fit

Liu, Kaixuan; Zhu, C.; Tao, Xuyuan; Bruniaux, Pascal; Zeng, Xianyi; Wang, J. P.

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.1007/s11042-023-14530-x

URL permanente :

http://hdl.handle.net/20.500.12210/84979

Titre :

A novel evaluation technique for human body perception of clothing fit

Auteur(s) :

Liu, Kaixuan [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Zhu, C. [Auteur]
Tao, Xuyuan [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Bruniaux, Pascal [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Zeng, Xianyi [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Wang, J. P. [Auteur]

Titre de la revue :

Multimedia Tools and Applications

Nom court de la revue :

Multimed. Tools Appl.

Numéro :

Pagination :

21057–21069

Date de publication :

2023-06

ISSN :

1380-7501

Mot(s)-clé(s) en anglais :

Fit evaluation
Decision tree C4
5
Machine learning
Fashion design
Data learning

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Fit evaluation plays an important role in garment products development and sales. Effective clothing fit evaluation methods can reduce the development cost of apparel products and the return rate of online apparel sales. ...
Lire la suite >Fit evaluation plays an important role in garment products development and sales. Effective clothing fit evaluation methods can reduce the development cost of apparel products and the return rate of online apparel sales. In this research, we proposed an intelligent fit evaluation technology to predict clothing fit. The mathematical relationship model between clothing fit levels and indexes reflecting the clothing fit levels was constructed by using decision tree C4.5 algorithm. Then, two experiments were carried out to collect input and output training data. After learning from the collected data, the proposed model can predict clothing fit accurately. Next, we validated our proposed model’s prediction accuracy using K-fold cross validation. Finally, we gave two applications of the proposed model for clothing products development and shopping online. Results show that our proposed method has high prediction accuracy and less requirement for the number of learning samples, and can predict clothing fit automatically and rapidly without real try-on.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

Génie des matériaux textiles (GEMTEX) - ULR 2641

Date de dépôt :

2023-06-20T12:12:18Z
2024-02-20T09:55:02Z

Numéro de version	Lien	Date de modification
2	20.500.12210/84979*	2024-02-20T09:52:10Z
1	20.500.12210/84979.1	2023-06-20T12:12:18Z

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A novel evaluation technique for human ...

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