Title: What makes a Pollock Pollock: a machine vision approach
Author: Lior Shamir
Address: Department of Math and Computer Science, Lawrence Technological University, 21000 W Ten Mile Rd., Southfield, MI 48075, USA
Abstract: Jackson Pollock introduced a revolutionary artistic style of dripping paint on a horizontal canvas. Here we study Pollock's unique artistic style by using computational methods for characterising the low-level numerical differences between original Pollock drip paintings and drip paintings of other painters who attempted to mimic his signature drip painting style. Four thousands and twenty four numerical image content descriptors were extracted from each painting, and compared using weighted nearest neighbour classification such that the Fisher discriminant scores of the content descriptors were used as weights. In 93% of the cases, the computer analysis was able to differentiate between an original and a non-original Pollock drip painting. The most discriminative image content descriptors that were unique to the work of Pollock were the fractal features, but other numerical image content descriptors such as Zernike polynomials, Haralick textures, and Chebyshev statistics show substantial differences between original and non-original Pollock drip paintings. These experiments show that the uniqueness of Pollock's drip painting style is not reflected merely by fractals, but also by other numerical image content descriptors that reflect the visual content. The code and software used for the experiment is publicly available, and can be used to study the work of other artists.
Keywords: Jackson Pollock; drip paintings; original art; computer vision; weighted nearest neighbour; fractal features; numerical image content descriptors; Zernike polynomials; Haralick textures; Chebyshev statistics; machine vision; art authentication.
Int. J. of Arts and Technology, 2015 Vol.8, No.1, pp.1 - 10
Available online: 08 Feb 2015