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Computational aesthetics and applications
Yihang Bo1, Jinhui Yu2, Kang Zhang3
1Department of Fine Art, Beijing Film Academy, Beijing, 100088, China.
Visual Computing for Industry, Biomedicine, and Art
|April 3, 2020
Summary
Computational aesthetics merges science and art, enabling computers to measure beauty and generate art. This study reviews aesthetic quantification and generative art, proposing a novel design generation framework.
Area of Science:
- Interdisciplinary field bridging computer science and art.
- Focuses on computational methods for aesthetic expression and creation.
Background:
- Computational aesthetics is an emerging field with broad applications.
- Existing research covers aesthetic measurement, quantification, and generative art techniques.
Purpose of the Study:
- To review current approaches in aesthetic measurement and quantification.
- To explore generative art, including fractal art and style-based abstract paintings.
- To propose a design generation framework integrating these aspects.
Main Methods:
- Review of features, evaluation methods, and applications in aesthetic measurement.
- Analysis of fractal art and style-transfer generative art.
- Development of a design generation methodology.
Main Results:
- Comprehensive overview of aesthetic measurement techniques and their applications.
- Exploration of generative art methods for creating fractal and style-imitating artworks.
- Proposal of an integrated design generation framework.
Conclusions:
- Computational aesthetics offers powerful tools for analyzing and creating visual art.
- The proposed framework integrates measurement and generation for automated design.
- This field has significant potential across various design disciplines.
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