Color–material furnishing pairing is known as a “black-box” for interior designers. The overall atmosphere of a space can be changed by modifying furnishing combinations, for example, to express modern or classic styles. Designers carefully choose pairings of colors and materials that fit their intended interior design styles based on experience and knowledge. However, no specific principles or rules have yet been established. Therefore, this study aims to derive a furnishing pairing principle based on a novel framework comprising object detection, color extraction, material recognition, and network analysis. We used the proposed framework to analyze large-scale interior design image data (N = 24,194) collected from an online interior design platform. We also used the authenticity algorithm to analyze the relative influence of styles. By using the data-driven method from large-scale data in each of the eight interior styles, we derived authentic color, material, and furnishing combinations. Our study results revealed that images with high authenticity values in each style matched existing style descriptions. Additionally, the proposed framework allows interior style image retrieval based on a specific color, material, and furnishing combination. Our findings have implications for research on the development of style-aware furniture retrieval systems and automatic interior design generation methods.

No abstract