Construction of womenswear matching expert system based on domain knowledge. Issue 5 (March 2023)

• Record Type: Journal Article
• Authors: Ma, Ling; Li, Tao; Guo, Ziyi; Lyu, Yexin; Zou, Fengyuan
• Is Part Of: Textile research journal. Volume 93:Issue 5/6(2023)
• Page Start: 1196
• Page End: 1210
• Publication Date: 2023-03
• Subjects: Automatic clothing matching -- domain knowledge -- expert system -- Prolog -- knowledge representation -- inference engine; Textile industry -- Periodicals; Textile fabrics -- Periodicals; Textile research -- Periodicals; Textiles et tissus -- Industrie et commerce -- Périodiques; Textiles et tissus -- Périodiques; Textiles et tissus -- Recherche -- Périodiques; Electronic journals; 677.005
• DOI: 10.1177/00405175221128619
• Languages: English
• ISSNs: 0040-5175

Abstract

Clothing matching refers to the coordination of style, color, etc. to achieve a decent and generous effect. With the development of artificial intelligence, increasing research efforts have been dedicated to complementary garment collocation as matching clothes to make a suitable outfit has become a daily headache for many people. However, existing studies neglect rules regarding clothing matching, which are based on the knowledge accumulated in the fashion domain and are implicitly transmitted and ambiguous. Towards this end, this article proposes an expert system based on domain knowledge implemented using the Prolog program to realize rule-guided clothing collocation. The article constructs clothing matching rules from four essential clothing attributes: season, type, style, and color, and uses Prolog syntax for knowledge representation. For the formulated facts and rules, the reasoning machine iteratively matches the user's instructions with the system knowledge to reason out suitable matching suggestions. In this study we built a website as a man-machine interface to facilitate friendly interaction and set season, type, style, and color sub-options to match clothing and satisfy user preferences. The system validation based on standard metrics (precision, recall, F1-measure) achieves results above 81%. The main contribution is that the system could match clothes more accurately according to the likes and requirements of users. After comparing with other research, the system is expected to provide suggestions or references for people to choose style and dressing.