Evolution of Aesthetically Pleasing Images Without Human-In-The-Loop
Inhaltsverzeichnis
Reference
Atkins, D., Klapaukh, R., Browne, W., Mengjie, M.: Evolution of Aesthetically Pleasing Images Without Human-In-The-Loop. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2010).
DOI
http://dx.doi.org/10.1109/CEC.2010.5586283
Abstract
Evolutionary Art is a sub-field of Evolutionary Computing that involves creating interesting images using Evolutionary Techniques. Previously Genetic Programming has been used to create such images autonomously - that is, without a human in the loop. However, this work did not explore alternative fitness measures, consider colour in fitness or provide independent validation of results. Four fitness functions based on the concept that the pleasingness of an image is based on the ratio of image complexity to processing complexity are explored. We introduce the use of Shannon Entropy as a measure of image complexity to compare with Jpeg Compression. Similarly, we introduce Run Length Encoding to compare with Fractal Compression as a measure of processing complexity. A survey of 100 participants showed that it is possible to generate aesthetically pleasing graphics using each fitness function. Importantly, it was the introduction of colour that separated the aesthetic effects of the fitness measures.
Extended Abstract
Bibtex
Used References
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