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.

通过应用模糊逻辑和图像处理技术，本研究介绍了一种新的方法来量化和预测室内设计的审美偏好，以综合考虑用户的色彩选择和整体审美吸引力。通过收集社交媒体平台上的室内设计图像，结合颜色和视觉属性，计算整体偏好分数，从而帮助设计师和专业人士更好地了解和满足人们在数字媒体时代对室内设计的偏好。