Interior Design Network of Furnishing and Color Pairing with Object Detection and Color Analysis Based on Deep Learning

Abstract

Furnishing is one of the most important interior design elements when decorating a space. Because every interior design element is colored, it is essential to consider the pairing of furnishing and color during the design process. Despite the importance of the furnishing and color pairing, the decision-making process by which the pairings are made remains a “black-box” of the interior design process. However, the advancement of social networks and online interior-design platforms such as Today’s House allows collecting large quantities of actual interior design cases that can be shared publicly. In addition, it has become possible to extract various features and relationships of data through machine learning techniques and network analysis. Thus, this paper proposes a data-driven approach to reveal distinct patterns of furnishing and color pairing through object detection, color extraction, and network analysis. To do that, we collected a large quantity of image data (N = 14,111) from Today’s House (ohou.se) online interior-design platform. Then, we extracted furnishing objects and color palettes from the collected images using object detection and color extraction algorithms. Finally, we identified distinctive patterns of furnishing and color pairing through network analysis.