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Information 2017, 8(2), 68;

Computer-Generated Abstract Paintings Oriented by the Color Composition of Images

Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu 610064, China
College of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
Author to whom correspondence should be addressed.
Received: 31 March 2017 / Revised: 15 June 2017 / Accepted: 15 June 2017 / Published: 20 June 2017
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Designers and artists often require reference images at authoring time. The emergence of computer technology has provided new conditions and possibilities for artistic creation and research. It has also expanded the forms of artistic expression and attracted many artists, designers and computer experts to explore different artistic directions and collaborate with one another. In this paper, we present an efficient k-means-based method to segment the colors of an original picture to analyze the composition ratio of the color information and calculate individual color areas that are associated with their sizes. This information is transformed into regular geometries to reconstruct the colors of the picture to generate abstract images. Furthermore, we designed an application system using the proposed method and generated many works; some artists and designers have used it as an auxiliary tool for art and design creation. The experimental results of datasets demonstrate the effectiveness of our method and can give us inspiration for our work. View Full-Text
Keywords: graphics processing; visual aesthetic; abstract painting; algorithm painting graphics processing; visual aesthetic; abstract painting; algorithm painting

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Li, M.; Lv, J.; Li, X.; Yin, J. Computer-Generated Abstract Paintings Oriented by the Color Composition of Images. Information 2017, 8, 68.

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