TY - JOUR
T1 - Intelligent color scheme generation for web interface color design based on knowledge − data fusion method
AU - Liu, Xin
AU - Yang, Zijuan
AU - Gong, Lin
AU - Liu, Minxia
AU - Xiang, Xi
AU - Mo, Zhenchong
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/5
Y1 - 2025/5
N2 - Diverse design requirements and the high dependency on artistic knowledge of designers make determining harmonious color schemes for web interface design challenging, calling for high-quality automatic color scheme generation. Yet, current studies are often limited to either data-driven approaches or art theories. In this paper, a conditional generative adversarial network (CGAN)-based color scheme generation method, CS-Ganerator, is proposed by integrating both knowledge and data to enable the automatic generation of color schemes for web interface design. Initially, an improved K-Means clustering algorithm is proposed and used to extract color scheme instances from a large image dataset with diverse themes. Subsequently, a CGAN model augmented with knowledge modules is employed to learn the underlying color and thematic relationships under aesthetic principles, enabling the generation of thematic color schemes. The generated schemes are then evaluated and filtered for harmony based on color theory, and categorized by warmth, darkness, and gradient to realize customized color preferences. The experimental results validate that the proposed CS-Ganerator can effectively generate diverse color schemes that highly match with the specific theme. The data and code are available at https://github.com/mzzdxg/CS-Ganerator.
AB - Diverse design requirements and the high dependency on artistic knowledge of designers make determining harmonious color schemes for web interface design challenging, calling for high-quality automatic color scheme generation. Yet, current studies are often limited to either data-driven approaches or art theories. In this paper, a conditional generative adversarial network (CGAN)-based color scheme generation method, CS-Ganerator, is proposed by integrating both knowledge and data to enable the automatic generation of color schemes for web interface design. Initially, an improved K-Means clustering algorithm is proposed and used to extract color scheme instances from a large image dataset with diverse themes. Subsequently, a CGAN model augmented with knowledge modules is employed to learn the underlying color and thematic relationships under aesthetic principles, enabling the generation of thematic color schemes. The generated schemes are then evaluated and filtered for harmony based on color theory, and categorized by warmth, darkness, and gradient to realize customized color preferences. The experimental results validate that the proposed CS-Ganerator can effectively generate diverse color schemes that highly match with the specific theme. The data and code are available at https://github.com/mzzdxg/CS-Ganerator.
KW - Color scheme generation
KW - Color theory
KW - Conditional generative adversarial network
KW - Knowledge − data fusion
KW - Web interface design
UR - http://www.scopus.com/inward/record.url?scp=85215409421&partnerID=8YFLogxK
U2 - 10.1016/j.aei.2024.103105
DO - 10.1016/j.aei.2024.103105
M3 - Article
AN - SCOPUS:85215409421
SN - 1474-0346
VL - 65
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
M1 - 103105
ER -