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The Evolution of Artistic Filters

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Part of the Natural Computing Series book series (NCS)

Summary

Artistic image filters are evolved using genetic programming. The system uses automatic image analysis during fitness evaluation. Multi-objective optimization permits multiple feature tests to be applied independently. One unique fitness test is Ralph’s bell curve model of aesthetics. This model is based on an empirical evaluation of hundreds of fine art works, in which paintings have been found to exhibit a bell curve distribution of color gradient. We found that this 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. Experiments resulted in a surprising 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.

Keywords

  • Multiobjective Optimization
  • Training Image
  • Source Image
  • Target Image
  • Current Pixel

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Neufeld, C., Ross, B.J., Ralph, W. (2008). The Evolution of Artistic Filters. In: Romero, J., Machado, P. (eds) The Art of Artificial Evolution. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72877-1_16

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  • DOI: https://doi.org/10.1007/978-3-540-72877-1_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72876-4

  • Online ISBN: 978-3-540-72877-1

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