title: Quantification of artistic style through sparse coding analysis in the drawings of Pieter Bruegel the Elder.
abstract: 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.
authors.affiliation: Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.
authors.forename: James M
authors.initials: JM
authors.lastname: Hughes
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Graham
authors.forename: Daniel N
authors.initials: DN
authors.lastname: Rockmore