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And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model

Author

Listed:
  • Baixi Xing
  • Kejun Zhang
  • Lekai Zhang
  • Xinda Wu
  • Huahao Si
  • Hui Zhang
  • Kaili Zhu
  • Shouqian Sun

Abstract

Aesthetic perception is a human instinct that is responsive to multimedia stimuli. Giving computers the ability to assess human sensory and perceptual experience of aesthetics is a well-recognized need for the intelligent design industry and multimedia intelligence study. In this work, we constructed a novel database for the aesthetic evaluation of design, using 2,918 images collected from the archives of two major design awards, and we also present a method of aesthetic evaluation that uses machine learning algorithms. Reviewers’ ratings of the design works are set as the ground-truth annotations for the dataset. Furthermore, multiple image features are extracted and fused. The experimental results demonstrate the validity of the proposed approach. Primary screening using aesthetic computing can be an intelligent assistant for various design evaluations and can reduce misjudgment in art and design review due to visual aesthetic fatigue after a long period of viewing. The study of computational aesthetic evaluation can provide positive effect on the efficiency of design review, and it is of great significance to aesthetic recognition exploration and applications development.

Suggested Citation

  • Baixi Xing & Kejun Zhang & Lekai Zhang & Xinda Wu & Huahao Si & Hui Zhang & Kaili Zhu & Shouqian Sun, 2020. "And the nominees are: Using design-awards datasets to build computational aesthetic evaluation model," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-19, January.
    • Handle: RePEc:plo:pone00:0227754
    DOI: 10.1371/journal.pone.0227754
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    References listed on IDEAS

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    1. Bloch, Peter H & Brunel, Frederic F & Arnold, Todd J, 2003. "Individual Differences in the Centrality of Visual Product Aesthetics: Concept and Measurement," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 29(4), pages 551-565, March.
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