Document Type


Publication Date


Publication Title

Proceedings of the National Academy of Sciences of the United States of America


Department of Computer Science


Recently, statistical techniques have been used to assist art historians in the analysis of works of art. We present a novel technique for the quantification of artistic style that utilizes a sparse coding model. Originally developed in vision research, sparse coding models can be trained to represent any image space by maximizing the kurtosis of a representation of an arbitrarily selected image from that space. We apply such an analysis to successfully distinguish a set of authentic drawings by Pieter Bruegel the Elder from another set of well-known Bruegel imitations. We show that our approach, which involves a direct comparison based on a single relevant statistic, offers a natural and potentially more germane alternative to wavelet-based classification techniques that rely on more complicated statistical frameworks. Specifically, we show that our model provides a method capable of discriminating between authentic and imitation Bruegel drawings that numerically outperforms well-known existing approaches. Finally, we discuss the applications and constraints of our technique.



Original Citation

Hughes JM, Graham DJ, Rockmore DN. Quantification of artistic style through sparse coding analysis in the drawings of Pieter Bruegel the Elder. Proc Natl Acad Sci U S A. 2010 Jan 26;107(4):1279-83. doi: 10.1073/pnas.0910530107. Epub 2010 Jan 5. PMID: 20080588; PMCID: PMC2824352.