Intelligent color scheme generation for web interface color design based on knowledge − data fusion method

Abstract

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.