Aesthetic Preference Prediction in Interior Design: Fuzzy Approach (2401.17710v1)

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.

• The paper introduces a fuzzy logic model that computes an aesthetic score by combining image-derived features like color harmony, lightness, and complexity.
• It integrates user-sampled color preferences with computational metrics using a fuzzy inference system to deliver a comprehensive aesthetic evaluation.
• Experimental validation via the Two-Alternative Forced Choice method achieved a 0.7 hit rate, highlighting the model’s robust predictive accuracy.

Aesthetic Preference Prediction in Interior Design: Fuzzy Approach

The paper "Aesthetic Preference Prediction in Interior Design: Fuzzy Approach" presents a methodological framework for assessing aesthetic preferences in interior design using fuzzy logic and image processing techniques. Its primary focus is the quantification and prediction of aesthetic appeal, which traditionally poses challenges due to its subjective nature. This research effectively integrates computational methods to bridge the gap between subjective aesthetic evaluation and objective analysis.

Methodology Overview

The authors provide a novel approach by leveraging fuzzy logic to quantify ambiguous concepts like color harmony, lightness, and complexity. They gathered a dataset of interior design images from social media platforms, recognizing the influence of digital media on public aesthetic preferences. The paper quantifies visual attributes and integrates them into an aesthetic score, combining this with individual color preferences sampled from user ratings of primary colors. These quantitative metrics are then input into a fuzzy inference system to compute an overall preference score.

The process of evaluating aesthetic preferences comprises several key steps:

1. Feature Extraction: Utilizing techniques from image processing, the paper derives key aesthetic features—Color Harmony (CH), Lightness (L), and Complexity (C)—from the image dataset. These features are then normalized for comparison.
2. Aesthetic Score Calculation: A weighted average calculation integrates the normalized features, emphasizing Lightness due to its impact on visual comfort.
3. Color Preference Evaluation: Single color preferences are extracted based on user ratings and pixel analysis of dominant colors in the images.
4. Fuzzy Inference System: The paper employs a fuzzy inference system that takes the aesthetic score and color preference as inputs, applying a set of well-defined fuzzy rules to produce a comprehensive preference score.

Experimental Validation

For empirical validation, the paper adopts the Two-Alternative Forced Choice (2AFC) method, achieving a 0.7 hit rate. This demonstrates the model's capability to predict user preferences with appreciable accuracy. The use of 2AFC highlights the system's robustness in discerning aesthetic appeal through direct user choices, solidifying the reliability of fuzzy logic in handling subjective data.

Implications and Future Directions

The research has significant implications for enhancing design processes. By providing a structured approach to aesthetic evaluation, it extends practical utility to design professionals aiming to align aesthetic elements with user preferences. The theoretical framework of this paper bridges computational and subjective realms, thus paving the way for advanced aesthetic modeling in design-related fields.

Looking forward, the methodology heralds potential applications beyond interior design, encompassing domains such as fashion, automotive aesthetics, and digital content creation. Future research could expand dataset size and participant diversity to refine accuracy further. Additionally, integrating advanced machine learning techniques with fuzzy logic could enhance predictive capabilities.

In summation, this paper offers an insightful advancement in the computational understanding of aesthetic preferences, emphasizing the role of fuzzy logic and well-curated datasets. As AI continues to influence creative industries, methodologies like these will be pivotal in harmonizing computational models with human-centric design paradigms.