Title: Web-based traditional craft Kansei retrieval method using a machine learning model and feature extraction

Authors: Naho Kuriya; Kaisei Komoto; Tomoyuki Ishida

Addresses: Graduate School of Engineering, Fukuoka Institute of Technology, Fukuoka, Japan ' Graduate School of Engineering, Fukuoka Institute of Technology, Fukuoka, Japan ' Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

Abstract: In a previous study, we conducted a Kansei analysis of traditional crafts using the semantic differential method and correlated visual features with physical features extracted via visual pattern image coding (VPIC) from images of craftwork. However, associating the VPIC features with visual attributes is labour-intensive. Therefore, this study proposes a new Kansei retrieval method for traditional crafts. This method utilises a machine learning model trained on the previous study's data to predict Kansei words based on features extracted from colour histograms and colour moments of craft images. To evaluate the proposed method, we conducted a questionnaire survey with 52 university students. Approximately 90% of the participants responded positively to the operability, visibility, relevance, and effectiveness of the entire system, and the functionality of the Kansei word retrieval function, while approximately 20% responded negatively to the functionality of the Kansei word prediction function.

Keywords: web application; Kansei retrieval; Kansei prediction; machine learning model; feature extraction.

DOI: 10.1504/IJWGS.2025.147092

International Journal of Web and Grid Services, 2025 Vol.21 No.2, pp.113 - 137

Received: 24 Jan 2025
Accepted: 21 Feb 2025
Published online: 10 Jul 2025 *

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