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Predicting beauty: fractal dimension and visual complexity in art
A Forsythe1, M Nadal, N Sheehy
1Department of Psychology, Aberystwyth University, Aberystwyth, UK. aof@aber.ac.uk
British Journal of Psychology (London, England : 1953)
|January 19, 2011
Summary
Automated measures, including GIF compression, can predict visual complexity in art. Fractal dimension better predicts perceived beauty, especially in abstract and natural images, but color is essential for beauty judgments.
Area of Science:
- Art and Aesthetics
- Computer Vision
- Psychology
Background:
- Visual complexity is a known predictor of art preference.
- Automated complexity measures are increasingly used in art analysis.
Purpose of the Study:
- To assess if automated measures predict perceived visual complexity in art.
- To determine if fractal dimension predicts perceived beauty in art.
- To investigate the role of color in aesthetic judgments.
Main Methods:
- Study 1: Evaluated various automated complexity measures, including GIF compression, against perceived visual complexity.
- Study 2: Assessed fractal dimension's ability to predict perceived beauty, comparing it with visual complexity measures.
- Color was systematically removed from images in a subset of the experiment.
Main Results:
- GIF compression emerged as the most effective predictor of visual complexity, contrary to prior research.
- Fractal dimension explained more variance in perceived beauty judgments than visual complexity measures alone.
- Image beauty judgments were not meaningful when color was absent.
Conclusions:
- Automated measures, particularly GIF compression, can quantify visual complexity in art.
- Fractal dimension is a valuable metric for predicting aesthetic beauty, especially for natural and abstract art.
- Color is a critical component for human observers making aesthetic judgments about art.
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