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AESTHETICS

Beauty is in the eye of the machine

Ansel Adams said, “There are no rules for good photographs, there are only good photographs.” Is it possible to predict our fickle and subjective appraisal of ‘aesthetically pleasing’ visual art? Iigaya et al. used an artificial intelligence approach to show how human aesthetic preference can be partially explained as an integration of hierarchical constituent image features.

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Fig. 1: “Edmond de Belamy, from La Famille de Belamy”.

References

  1. Kant, I. & Bernard, J. H. Kant’s Critique of Judgement. 2d ed. (Macmillan, 1931).

  2. Kong, S., Shen, X., Lin, Z., Mech, R. & Fowlkes, C. Photo aesthetics ranking network with attributes and content adaptation. in European Conference on Computer Vision (eds Leibe B., Matas J., Sebe N. & Welling M.) 662–679 (2016).

  3. Iigaya, K., Yi, S., Wahle, I., Tanwisuth, K. & O’Doherty, J. Aesthetic preference for art can be predicted from a mixture of low- and high-level visual features Nat. Hum. Behav. https://doi.org/10.1038/s41562-021-01124-6 (2021).

  4. Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. Preprint at arXiv https://arxiv.org/abs/1409.1556 (2015).

  5. Kreiman, G. Biological and Computer Vision (Cambridge Univ. Press, 2021).

  6. Russakovsky, O., Li, L.-J. & Li, F.-F. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2121–2131 (Computer Vision Foundation, 2014).

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Correspondence to Gabriel Kreiman.

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Zhang, M., Kreiman, G. Beauty is in the eye of the machine. Nat Hum Behav 5, 675–676 (2021). https://doi.org/10.1038/s41562-021-01125-5

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