AccScience Publishing / AC / Volume 2 / Issue 2 / DOI: 10.36922/ac.1628
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Computer vision profiling and identification of authentic Jackson Pollock drip paintings

Lior Shamir1*
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1 Department of Computer Science, Kansas State University, Manhattan, Kansas, United States of America
Submitted: 18 August 2023 | Accepted: 23 November 2023 | Published: 15 February 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

The drip paintings of Jackson Pollock introduced a unique style of abstract expressionism that has attracted substantial interest. Here, a quantitative approach to the analysis of Jackson Pollock’s drip paintings was applied. The analysis was performed using quantitative elements from authentic Jackson Pollock paintings, and comparing them to the same quantitative elements computed from fake Jackson Pollock paintings. Profiling the differences can lead to the identification of unique mathematical elements that characterize Jackson Pollocks artistic style. These elements provide a new way of examining and analyzing the visual content of art. That quantitative analysis can expand on the qualitative analysis methods and provide new insights about visual art. The results show that authentic Jackson Pollock paintings can be differentiated from faked Jackson Pollock paintings in ∼96% of the cases. While fractals provide the strongest differences between authentic and faked Pollock paintings, the analysis also reveals other elements of the paintings that are unique to Jackson Pollock’s style. These elements include textures, entropy, and the distribution of light.

Keywords
Pollock
Drip paintings
Image analysis
Quantitative analysis
Art authentication
Funding
NSF grant 2148878.
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Conflict of interest
The author declares no competing interests.
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Arts & Communication, Electronic ISSN: 2972-4090 Published by AccScience Publishing