The Evolution of Artistic Filters

by C. Neufeld, B.J. Ross, W. Ralph

Artistic image filters are evolved using genetic programming. A number of image analyses are done during fitness evaluation, such as Ralph's model of aesthetics, and colour matching. Multi-objective optimisation permits multiple feature tests to be applied independently. We found that Ralph's test is very useful for automatically evolving non-photorealistic filters that tend to produce images with painterly, balanced and harmonious characteristics. The genetic programming language uses a variety of image processing functions of varying complexity, including a higher-level paint stroke operator. The filter language is designed so that components can be combined together in complex and unexpected ways. Runs resulted in a variety of interesting ``artistic filters'', which tend to function more like higher-level artistic processes than low-level image filters. Furthermore, a correlation was found between an image having a good aesthetic score, and its application of the paint operator.



Images copyright 2006 C. Neufeld and B.J. Ross.
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