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Large-Scale Quantitative Analysis of Painting Arts

Scientists have made efforts to understand the beauty of painting art in their own languages. As digital image acquisition of painting arts has made rapid progress, researchers have come to a point where it is possible to perform statistical analysis of a large-scale database of artistic paints to m...

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Detalles Bibliográficos
Autores principales: Kim, Daniel, Son, Seung-Woo, Jeong, Hawoong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4263068/
https://www.ncbi.nlm.nih.gov/pubmed/25501877
http://dx.doi.org/10.1038/srep07370
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author Kim, Daniel
Son, Seung-Woo
Jeong, Hawoong
author_facet Kim, Daniel
Son, Seung-Woo
Jeong, Hawoong
author_sort Kim, Daniel
collection PubMed
description Scientists have made efforts to understand the beauty of painting art in their own languages. As digital image acquisition of painting arts has made rapid progress, researchers have come to a point where it is possible to perform statistical analysis of a large-scale database of artistic paints to make a bridge between art and science. Using digital image processing techniques, we investigate three quantitative measures of images – the usage of individual colors, the variety of colors, and the roughness of the brightness. We found a difference in color usage between classical paintings and photographs, and a significantly low color variety of the medieval period. Interestingly, moreover, the increment of roughness exponent as painting techniques such as chiaroscuro and sfumato have advanced is consistent with historical circumstances.
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spelling pubmed-42630682014-12-16 Large-Scale Quantitative Analysis of Painting Arts Kim, Daniel Son, Seung-Woo Jeong, Hawoong Sci Rep Article Scientists have made efforts to understand the beauty of painting art in their own languages. As digital image acquisition of painting arts has made rapid progress, researchers have come to a point where it is possible to perform statistical analysis of a large-scale database of artistic paints to make a bridge between art and science. Using digital image processing techniques, we investigate three quantitative measures of images – the usage of individual colors, the variety of colors, and the roughness of the brightness. We found a difference in color usage between classical paintings and photographs, and a significantly low color variety of the medieval period. Interestingly, moreover, the increment of roughness exponent as painting techniques such as chiaroscuro and sfumato have advanced is consistent with historical circumstances. Nature Publishing Group 2014-12-11 /pmc/articles/PMC4263068/ /pubmed/25501877 http://dx.doi.org/10.1038/srep07370 Text en Copyright © 2014, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle Article
Kim, Daniel
Son, Seung-Woo
Jeong, Hawoong
Large-Scale Quantitative Analysis of Painting Arts
title Large-Scale Quantitative Analysis of Painting Arts
title_full Large-Scale Quantitative Analysis of Painting Arts
title_fullStr Large-Scale Quantitative Analysis of Painting Arts
title_full_unstemmed Large-Scale Quantitative Analysis of Painting Arts
title_short Large-Scale Quantitative Analysis of Painting Arts
title_sort large-scale quantitative analysis of painting arts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4263068/
https://www.ncbi.nlm.nih.gov/pubmed/25501877
http://dx.doi.org/10.1038/srep07370
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