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Proceedings Paper

Stylometrics of artwork: uses and limitations
Author(s): James M. Hughes; Daniel J. Graham; Daniel N. Rockmore
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Paper Abstract

A number of digital image analysis techniques have been developed in recent years to address art historical questions. These techniques allow non-destructive analyses of art images that can target outstanding problems of attribution, historical ordering, and other stylistic dimensions. However, great care must be taken in designing the comparisons to which these techniques are applied. In this paper, we review recent work by our lab and by others aimed at establishing a toolbox of stylometrics, and we discuss some of the uses and limitations of these methods. We describe a technique that provides robust classification of authentic drawings by Pieter Bruegel the Elder, and we demonstrate new techniques for art historical analysis applied to the works of other masters. Specifically, we demonstrate the use of two low-level statistics (the slope of the log amplitude spectrum and color histogram correlation) to analyze the works of Picasso and Braque. Finally, we show that face detection and recognition techniques may play a useful role in the attribution of works of art. The rationale for employing vision coding-like methods (e.g., sparse coding) in stylometry is also reviewed. We conclude that generic authentication tools are unlikely to provide reliable stylometric predictions but that with careful construction of comparison sets - which we believe must be done in close collaboration with art historians - these techniques provide important predictions that can be weighed against other art historical evidence. We argue further that concurrent predictions derived from analysis of many independent dimensions of image data (e.g., color, luminance, and spatial statistics) provide the strongest evidence for digital stylometric determinations.

Paper Details

Date Published: 16 February 2010
PDF: 15 pages
Proc. SPIE 7531, Computer Vision and Image Analysis of Art, 75310C (16 February 2010); doi: 10.1117/12.838849
Show Author Affiliations
James M. Hughes, Dartmouth College (United States)
Daniel J. Graham, Dartmouth College (United States)
Daniel N. Rockmore, Dartmouth College (United States)

Published in SPIE Proceedings Vol. 7531:
Computer Vision and Image Analysis of Art
David G. Stork; Jim Coddington; Anna Bentkowska-Kafel, Editor(s)

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