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Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach

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Listed:
  • Adrian Carballal
  • Carlos Fernandez-Lozano
  • Nereida Rodriguez-Fernandez
  • Luz Castro
  • Antonino Santos

Abstract

An important topic in evolutionary art is the development of systems that can mimic the aesthetics decisions made by human begins, e.g., fitness evaluations made by humans using interactive evolution in generative art. This paper focuses on the analysis of several datasets used for aesthetic prediction based on ratings from photography websites and psychological experiments. Since these datasets present problems, we proposed a new dataset that is a subset of DPChallenge.com. Subsequently, three different evaluation methods were considered, one derived from the ratings available at DPChallenge.com and two obtained under experimental conditions related to the aesthetics and quality of images. We observed different criteria in the DPChallenge.com ratings, which had more to do with the photographic quality than with the aesthetic value. Finally, we explored learning systems other than state-of-the-art ones, in order to predict these three values. The obtained results were similar to those using state-of-the-art procedures.

Suggested Citation

  • Adrian Carballal & Carlos Fernandez-Lozano & Nereida Rodriguez-Fernandez & Luz Castro & Antonino Santos, 2019. "Avoiding the Inherent Limitations in Datasets Used for Measuring Aesthetics When Using a Machine Learning Approach," Complexity, Hindawi, vol. 2019, pages 1-12, January.
    • Handle: RePEc:hin:complx:4659809
    DOI: 10.1155/2019/4659809
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    References listed on IDEAS

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    1. Manuela M Marin & Helmut Leder, 2013. "Examining Complexity across Domains: Relating Subjective and Objective Measures of Affective Environmental Scenes, Paintings and Music," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-1, August.
    2. Adrian Carballal & Luz Castro & Rebeca Perez & João Correia, 2014. "Detecting Bias on Aesthetic Image Datasets," International Journal of Creative Interfaces and Computer Graphics (IJCICG), IGI Global, vol. 5(2), pages 62-74, July.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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