Inferring compositional style in the neo-plastic paintings of Piet Mondrian by machine learning

David Andrzejewski; David G. Stork; Xiaojin Zhu; Ron Spronk

doi:10.1117/12.840558

16 February 2010 Inferring compositional style in the neo-plastic paintings of Piet Mondrian by machine learning

David Andrzejewski, David G. Stork, Xiaojin Zhu, Ron Spronk

Proceedings Volume 7531, Computer Vision and Image Analysis of Art; 75310G (2010) https://doi.org/10.1117/12.840558
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

Abstract

We trained generative models and decision tree classifiers with positive and negative examples of the neo-plastic works of Piet Mondrian to infer his compositional principles, to generate "faux" works, and to explore the possibility of computer-based aids in authentication and attribution studies. Unlike previous computer work on this and other artists, we used "earlier state" works-intermediate versions of works created by Mondrian revealed through x-radiography and infra-red reflectography-when training our classifiers. Such intermediate state works provide a great deal of information to a classifier as they differ only slightly from the final works. We used methods from machine learning such as leave-one-out cross validation. Our decision tree classifier had accuracy of roughly 70% in recognizing the genuine works of Mondrian versus computer-generated replicas with similar statistical properties. Our trained classifier reveals implicit compositional principles underlying Mondrian's works, for instance the relative visual "weights" of the four colors (red, yellow, blue and black) he used in his rectangles. We used our trained generative model to generate "faux" Mondrians, which informally possess some of the compositional attributes of genuine works by this artist.

Citation Download Citation

David Andrzejewski, David G. Stork, Xiaojin Zhu, and Ron Spronk "Inferring compositional style in the neo-plastic paintings of Piet Mondrian by machine learning", Proc. SPIE 7531, Computer Vision and Image Analysis of Art, 75310G (16 February 2010); https://doi.org/10.1117/12.840558

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