Using machine learning to distinguish between authentic and imitation Jackson Pollock poured paintings: A tile-driven approach to computer vision
Author:
Smith Julian H.ORCID,
Holt Caleb,
Smith Nickolaus H.,
Taylor Richard P.
Abstract
Jackson Pollock’s abstract poured paintings are celebrated for their striking aesthetic qualities. They are also among the most financially valued and imitated artworks, making them vulnerable to high-profile controversies involving Pollock-like paintings of unknown origin. Given the increased employment of artificial intelligence applications across society, we investigate whether established machine learning techniques can be adopted by the art world to help detect imitation Pollocks. The low number of images compared to typical artificial intelligence projects presents a potential limitation for art-related applications. To address this limitation, we develop a machine learning strategy involving a novel image ingestion method which decomposes the images into sets of multi-scaled tiles. Leveraging the power of transfer learning, this approach distinguishes between authentic and imitation poured artworks with an accuracy of 98.9%. The machine also uses the multi-scaled tiles to generate novel visual aids and interpretational parameters which together facilitate comparisons between the machine’s results and traditional investigations of Pollock’s artistic style.
Funder
Linde Martin Institute: https://www.lindemartin.com/
Ripple Group: https://rippleventures.co/about
Publisher
Public Library of Science (PLoS)
Reference79 articles.
1. Chaos, Fractals, Nature.;RP Taylor;Fractals Research,2010
2. AI identified a Renaissance masterpiece. Art historians are skeptical.;S. Avi-Yonah;Washington Post,2023
3. Painting and calligraphy identification method based on hyperspectral imaging and convolution neural network;X Tang;Spectrosc Lett,2021