Edited by David Mumford, Brown University, Providence, RI, and approved November 30, 2009 (received for review September 14, 2009)
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.
Author contributions: J.M.H., D.J.G., and D.N.R. designed research; J.M.H. and D.J.G. performed research; J.M.H., D.J.G., and D.N.R. contributed new reagents/analytic tools; J.M.H., D.J.G., and D.N.R. analyzed data; J.M.H., D.J.G., and D.N.R. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
