Research on Visual Perception Feature Extraction of Textile Color Matching and Clothing Display Optimization Strategies
Main Article Content
Abstract
Color is the most direct visual attribute of apparel products and plays an important role in attracting consumer attention, conveying brand concepts, and stimulating purchasing interest in terminal displays. This study develops an optimal color-matching method for apparel display by combining quantitative color analysis with visual perception experiments. Common apparel display images are first collected, and the main hue, contrast, and spatial distribution characteristics of color samples are extracted in HSV color space to form a quantitative description system for color matching. Because apparel color perception depends on visible-spectrum optical interaction and image feature extraction, the method provides an engineering-oriented basis for evaluating visual display effects. A visual perception experiment is then designed in which participants judge the visual saliency, harmony, and preference of different color matching patterns. Correlation analysis is used to explore the internal relationship between quantitative color characteristics and consumer visual perception, and targeted color optimization strategies for apparel displays are proposed. The results show that hue-difference ratio and brightness-difference ratio are positively correlated with visual saliency, while color matching with similar saturation is more likely to obtain higher harmony scores. Adjacent color matching based on the main hue receives higher preference scores, whereas high-purity complementary matching attracts attention rapidly but shows lower harmony.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
H. Zhang, X. Mu, G. Li, et al., “Mannequin2real: a two-stage generation framework for transforming mannequin images into photorealistic model images for clothing display,” IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 2773-2783, 2024, DOI: 10.1109/TCE.2024.3367790.
S. Chaouch, A. Moussa, and N. Ladhari, “Textile color formulation methods: A literature review,” Color Research & Application, vol. 50, no. 1, pp. 72-93, 2025, DOI: 10.1002/col.22953.
Y. Chae, “Spectrophotometric and calculative determination methods of textile color-difference threshold considering the observing condition,” Research Journal of Textile and Apparel, vol. 29, no. 4, pp. 1053-1070, 2025, DOI: 10.1108/RJTA-05-2024-0075.
J. Dong Z, F. Liang J, J. Zhang Z, et al., “The perceptual evaluation of clothing sustainable color in clothing design,” Journal of Fiber Bioengineering and Informatics, vol. 16, no. 3, pp. 229-241, 2023, DOI: 10.3993/jfbim02351.
T. Ichinose, Y. Pan, and Y. Yoshida, “Clothing color effect as a target of the smallest scale climate change adaptation,” International Journal of Biometeorology, vol. 68, no. 10, pp. 2029-2040, 2024, DOI: 10.1007/s00484-024-02726-1.
E. Lee, S. Moon, and Y. Chae, “Consumers’ Color Memory of Fashion Products in Live-Streaming E-commerce: Effects of Clothing Color and Color Combination,” Fibers and Polymers, vol. 26, no. 5, pp. 2233-2247, 2025, DOI: 10.1007/s12221-025-00921-4.
X. Su, J. Duan, J. Ren, et al., “Personalized clothing recommendation fusing the 4-season color system and users’ biological characteristics,” Multimedia tools and applications, vol. 83, no. 5, pp. 12597-12625, 2024, DOI: 10.1007/s11042-023-16014-4.
J. Zhou, X. Zou, and K. Wong W, “Computer vision-based color sorting for waste textile recycling,” International Journal of Clothing Science and Technology, vol. 34, no. 1, pp. 29-40, 2022, DOI: 10.1108/IJCST-12-2019-0190.
F. Wen, Y. Qiao, B. Zuo, et al., “Dominance or integration? Influence of sexual dimorphism and clothing color on judgments of male and female targets’ attractiveness, warmth, and competence,” Archives of Sexual Behavior, vol. 51, no. 6, pp. 2823-2836, 2022, DOI: 10.1007/s10508-021-02283-3.
Y. Park C, C. Lim B, J. Lee W, et al., “Suitable clothing recommendation system by size and skin color,” Journal of Digital Convergence, vol. 20, no. 3, pp. 407-413, 2022, DOI: 10.14400/JDC.2022.20.3.407.
W. Jiang, Y. Lu, P. Xu, et al., “Pattern Redesign Imitating Ethnic Clothing Color Styles via Palette-guided GAN,” ACM Journal on Computing and Cultural Heritage, vol. 18, no. 4, pp. 1-18, 2025, DOI: 10.1145/3771996.
D. Zhang Y and F. Jiang X, “The influence of clothing logo color on consumers’ green purchase intention,” Journal of Fiber Bioengineering and Informatics, vol. 17, no. 3, pp. 151-162, 2024, DOI: 10.3993/jfbim03062.
R. Ramli and D. Kalifia A, “Chatbot for Clothing Color Recommendations Based on Skin Tone,” bit-Tech, vol. 8, no. 2, pp. 2206-2217, 2025, DOI: 10.32877/bt.v8i2.3220.