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International Conference on Evolutionary and Biologically Inspired Music and Art

EvoMUSART 2012: Evolutionary and Biologically Inspired Music, Sound, Art and Design pp 35–47Cite as

Evolving a Library of Artistic Scene Descriptors

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7247)

Abstract

We describe the building of a library of 10,000 distinct abstract art images, and how these can be interpreted as describing the placement of objects in a scene for generative painting projects. Building the library to contain only markedly distinct images necessitated a machine learning approach, whereby two decision trees were derived to predict visual similarity in pairs of images. The first tree uses genotypical information to predict before image generation whether two images will be too similar. The second tree uses phenotypical information, namely how pairs of images differ when segmented using various distance thresholds. Taken together, the trees are highly effective at quickly predicting when two images are similar, and we used this in an evolutionary search where non-unique individuals are pruned, to build up the library. We show how the pruning approach can be used alongside a fitness function to increase the yield of images with certain properties, such as low/high colour variety, symmetry and contrast.

Keywords

  • Decision Tree
  • Evolutionary Search
  • Random Individual
  • Scene Descriptor
  • Computational Creativity

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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  • DOI: 10.1007/978-3-642-29142-5_4
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Author information

Authors and Affiliations

  1. Computational Creativity Group, Dept. of Computing, Imperial College, London, UK

    Simon Colton

Authors
  1. Simon Colton
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Department of Informatics Engineering, University of Coimbra, Pólo II - Pinhal de Marrocos, 3030, Coimbra, Portugal

    Penousal Machado

  2. School of Computer Science, Department of Communications and Information Technologies, University of A Coruña, Campus de Elviña, 15071 A, Coruña, Spain

    Juan Romero & Adrian Carballal & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Colton, S. (2012). Evolving a Library of Artistic Scene Descriptors. In: Machado, P., Romero, J., Carballal, A. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2012. Lecture Notes in Computer Science, vol 7247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29142-5_4

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  • DOI: https://doi.org/10.1007/978-3-642-29142-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29141-8

  • Online ISBN: 978-3-642-29142-5

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