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Open AccessArticle

Aesthetic Image Statistics Vary with Artistic Genre

University of Lincoln, School of Psychology, Lincoln LN6 7AY, UK
Vision 2020, 4(1), 10;
Received: 20 November 2019 / Revised: 28 January 2020 / Accepted: 29 January 2020 / Published: 1 February 2020
Research to date has not found strong evidence for a universal link between any single low-level image statistic, such as fractal dimension or Fourier spectral slope, and aesthetic ratings of images in general. This study assessed whether different image statistics are important for artistic images containing different subjects and used partial least squares regression (PLSR) to identify the statistics that correlated most reliably with ratings. Fourier spectral slope, fractal dimension and Shannon entropy were estimated separately for paintings containing landscapes, people, still life, portraits, nudes, animals, buildings and abstracts. Separate analyses were performed on the luminance and colour information in the images. PLSR fits showed shared variance of up to 75% between image statistics and aesthetic ratings. The most important statistics and image planes varied across genres. Variation in statistics may reflect characteristic properties of the different neural sub-systems that process different types of image.
Keywords: image statistics; spectral slope; fractal dimension; entropy; aesthetics image statistics; spectral slope; fractal dimension; entropy; aesthetics
MDPI and ACS Style

Mather, G. Aesthetic Image Statistics Vary with Artistic Genre. Vision 2020, 4, 10.

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