Aesthetic Preference Prediction in Interior Design: Fuzzy Approach
Ayana Adilova, Pakizar Shamoi
TL;DR
This work tackles the challenge of quantifying subjective interior-design aesthetics by integrating image-derived features with individual color preferences through a fuzzy inference system. It introduces a two-stage method: data collection from social media images and feature extraction (Color Harmony, Lightness, Complexity), followed by a weighted aesthetic score and a personalized $Total\ Preference$ via a nine-rule FIS. Key contributions include (i) a novel fusion of color-theory-based features with user color preferences, (ii) a practical dataset assembled from Instagram, and (iii) validation using a 2AFC task achieving a hit rate of $0.7$, supporting the method’s ability to reflect individual tastes. The approach offers a potential tool for designers and digital platforms to tailor interior-design recommendations and assessments in a real-world, media-rich context, with broader applicability to fashion, product design, and visual aesthetics evaluation.
Abstract
Interior design is all about creating spaces that look and feel good. However, the subjective nature of aesthetic preferences presents a significant challenge in defining and quantifying what makes an interior design visually appealing. The current paper addresses this gap by introducing a novel methodology for quantifying and predicting aesthetic preferences in interior design. Our study combines fuzzy logic with image processing techniques. We collected a dataset of interior design images from social media platforms, focusing on essential visual attributes such as color harmony, lightness, and complexity. We integrate these features using weighted average to compute a general aesthetic score. Our approach considers individual color preferences in calculating the overall aesthetic preference. We initially gather user ratings for primary colors like red, brown, and others to understand their preferences. Then, we use the pixel count of the top five dominant colors in the image to get the color scheme preference. The color scheme preference and the aesthetic score are then passed as inputs to the fuzzy inference system to calculate an overall preference score. This score represents a comprehensive measure of the user's preference for a particular interior design, considering their color choices and general aesthetic appeal. We used the 2AFC (Two-Alternative Forced Choice) method to validate our methodology, achieving a notable hit rate of 0.7. This study can help designers and professionals better understand and meet people's interior design preferences, especially in a world that relies heavily on digital media.