Personalized Aesthetic Assessment: Integrating Fuzzy Logic and Color Preferences

The analysis of aesthetic assessment is a complex and subjective task that has attracted researchers for a long time. The subjective nature of aesthetic preferences presents a significant challenge in defining and quantifying what makes images visually appealing. The current paper addresses this gap by introducing a novel methodology for quantifying and predicting aesthetic preferences in the case of interior design images. Our study combines fuzzy logic with image processing techniques. Firstly, a dataset of interior design images was collected from social media platforms, focusing on essential visual attributes such as color harmony, lightness, and complexity. Then, these features were integrated using a weighted average to compute a general aesthetic score. Our methodology considers personal color tastes when determining the overall aesthetic appeal. Initially, user feedback was collected on primary colors such as red, brown, and others to gauge their preferences. Subsequently, the image's five most prevalent colors were analyzed to determine the preferred color scheme based on pixel count. 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. The Two-Alternative Forced Choice (2AFC) method validated the methodology, resulting in a notable hit rate of 0.68. This study can help in fields such as art, design, advertising, or multimedia content creation, where aesthetic analysis and preference prediction are crucial. In the case of interior design, this study can help designers and professionals better understand and meet people's preferences, especially in a world that relies heavily on digital media.