Summary of Aesthetic Preference Prediction in Interior Design: Fuzzy Approach, by Ayana Adilova and Pakizar Shamoi
Aesthetic Preference Prediction in Interior Design: Fuzzy Approach
by Ayana Adilova, Pakizar Shamoi
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a novel methodology for quantifying and predicting aesthetic preferences in interior design using fuzzy logic and image processing techniques. The authors collected a dataset of interior design images from social media platforms, focusing on visual attributes like color harmony, lightness, and complexity. They integrated these features to compute an overall aesthetic score, considering individual color preferences. The approach uses pixel count of dominant colors to understand user preferences. The fuzzy inference system calculates an overall preference score, representing a comprehensive measure of user preference for a particular interior design. The methodology was validated using the 2AFC method, achieving a hit rate of 0.7. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand what makes interior designs look good or bad. It creates a special way to measure people’s preferences by looking at pictures and colors. First, it gathers user ratings for different colors and counts the top five most used colors in an image. Then, it uses fuzzy logic (a computer program) to combine these features and calculate a score that shows how much someone likes a design. The study tested this method and got good results! This can help designers make better designs that people like. |
Keywords
* Artificial intelligence * Inference
