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Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art

Several statistical image properties have been associated with large subsets of traditional visual artworks. Here, we investigate some of these properties in three categories of art that differ in artistic claim and prestige: (1) Traditional art of different cultural origin from established museums...

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Autores principales: Redies, Christoph, Brachmann, Anselm
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5660963/
https://www.ncbi.nlm.nih.gov/pubmed/29118692
http://dx.doi.org/10.3389/fnins.2017.00593
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author Redies, Christoph
Brachmann, Anselm
author_facet Redies, Christoph
Brachmann, Anselm
author_sort Redies, Christoph
collection PubMed
description Several statistical image properties have been associated with large subsets of traditional visual artworks. Here, we investigate some of these properties in three categories of art that differ in artistic claim and prestige: (1) Traditional art of different cultural origin from established museums and art collections (oil paintings and graphic art of Western provenance, Islamic book illustration and Chinese paintings), (2) Bad Art from two museums that collect contemporary artworks of lesser importance (© Museum Of Bad Art [MOBA], Somerville, and Official Bad Art Museum of Art [OBAMA], Seattle), and (3) twentieth century abstract art of Western provenance from two prestigious museums (Tate Gallery and Kunstsammlung Nordrhein-Westfalen). We measured the following four statistical image properties: the fractal dimension (a measure relating to subjective complexity); self-similarity (a measure of how much the sections of an image resemble the image as a whole), 1st-order entropy of edge orientations (a measure of how uniformly different orientations are represented in an image); and 2nd-order entropy of edge orientations (a measure of how independent edge orientations are across an image). As shown previously, traditional artworks of different styles share similar values for these measures. The values for Bad Art and twentieth century abstract art show a considerable overlap with those of traditional art, but we also identified numerous examples of Bad Art and abstract art that deviate from traditional art. By measuring statistical image properties, we quantify such differences in image composition for the first time.
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spelling pubmed-56609632017-11-08 Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art Redies, Christoph Brachmann, Anselm Front Neurosci Neuroscience Several statistical image properties have been associated with large subsets of traditional visual artworks. Here, we investigate some of these properties in three categories of art that differ in artistic claim and prestige: (1) Traditional art of different cultural origin from established museums and art collections (oil paintings and graphic art of Western provenance, Islamic book illustration and Chinese paintings), (2) Bad Art from two museums that collect contemporary artworks of lesser importance (© Museum Of Bad Art [MOBA], Somerville, and Official Bad Art Museum of Art [OBAMA], Seattle), and (3) twentieth century abstract art of Western provenance from two prestigious museums (Tate Gallery and Kunstsammlung Nordrhein-Westfalen). We measured the following four statistical image properties: the fractal dimension (a measure relating to subjective complexity); self-similarity (a measure of how much the sections of an image resemble the image as a whole), 1st-order entropy of edge orientations (a measure of how uniformly different orientations are represented in an image); and 2nd-order entropy of edge orientations (a measure of how independent edge orientations are across an image). As shown previously, traditional artworks of different styles share similar values for these measures. The values for Bad Art and twentieth century abstract art show a considerable overlap with those of traditional art, but we also identified numerous examples of Bad Art and abstract art that deviate from traditional art. By measuring statistical image properties, we quantify such differences in image composition for the first time. Frontiers Media S.A. 2017-10-25 /pmc/articles/PMC5660963/ /pubmed/29118692 http://dx.doi.org/10.3389/fnins.2017.00593 Text en Copyright © 2017 Redies and Brachmann. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Redies, Christoph
Brachmann, Anselm
Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art
title Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art
title_full Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art
title_fullStr Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art
title_full_unstemmed Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art
title_short Statistical Image Properties in Large Subsets of Traditional Art, Bad Art, and Abstract Art
title_sort statistical image properties in large subsets of traditional art, bad art, and abstract art
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5660963/
https://www.ncbi.nlm.nih.gov/pubmed/29118692
http://dx.doi.org/10.3389/fnins.2017.00593
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