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Finding Image Features Associated with High Aesthetic Value by Machine Learning

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Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7834))

Abstract

A major goal of evolutionary art is to get images of high aesthetic value. We assume that some features of images are associated with high aesthetic value and want to find them. We have taken two image databases that have been rated by humans, a photographic database and one of abstract images generated by evolutionary art software. We have computed 55 features for each database. We have extracted two categories of rankings, the lowest and the highest. Using feature extraction methods from machine learning we have identified the features most associated with differences. For the photographic images the key features are wavelet and texture features. For the abstract images the features are colour based features.

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Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, RMIT University, GPO Box 2476, Melbourne, Vic, 3000, Australia

    Vic Ciesielski & Perry Barile

  2. School of Media and Communication, RMIT University, GPO Box 2476, Melbourne, Vic, 3000, Australia

    Karen Trist

Authors
  1. Vic Ciesielski
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  2. Perry Barile
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  3. Karen Trist
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Editors and Affiliations

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

    Penousal Machado

  2. Quinn Business School, University College Dublin, Dublin 4, Donnybrook, Ireland

    James McDermott

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

    Adrian Carballal

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Ciesielski, V., Barile, P., Trist, K. (2013). Finding Image Features Associated with High Aesthetic Value by Machine Learning. In: Machado, P., McDermott, J., Carballal, A. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2013. Lecture Notes in Computer Science, vol 7834. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36955-1_5

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  • DOI: https://doi.org/10.1007/978-3-642-36955-1_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36954-4

  • Online ISBN: 978-3-642-36955-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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