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Proceedings Paper

Multiple visual features for the computer authentication of Jackson Pollock's drip paintings: beyond box counting and fractals
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Paper Abstract

Drip paintings by the American Abstract Expressionist Jackson Pollock have been analyzed through computer image methods, generally in support of authentication studies. The earliest and most thoroughly explored methods are based on an estimate of a "fractal dimension" by means of box-counting algorithms, in which the painting's image is divided into ever finer grids of boxes and the proportion of boxes containing some paint is counted. The plot of this proportion (on a log-log scale) reveals scaling or fractal properties of the work. These methods have been extended in a number of ways, including multifractal analysis, where an information measure replaces simple box paint occupancy. Recent studies suggest that it is unlikely that any single measure, including those based on such box counting, will yield highly accurate authentication; for example, a broad class of highly artificial angular sketches created in software reveal the same "fractal" properties as genuine Pollock paintings. Others have argued that this result precludes the value of such fractal-based features for such authentication. We show theoretically that even if a visual feature (taken alone) is "uninformative," such a feature can enhance discrimination when it is combined in a classifier with other features-even if these other features are themselves also individually uninformative. We describe simple classifiers for distinguishing genuine Pollocks from fakes based on multiple features such as fractal dimension, topological genus, "energy" in oriented spatial filters, and so forth. We trained linear-discriminant and nearest-neighbor classifiers using these features and found that our classifiers gave slightly improved recognition accuracy on human generated drip paintings. Most importantly, we found that although fractal features, taken alone might have low discriminative power, such features improved accuracy in multi-feature classifiers. We conclude that it is premature to reject the use of visual features based on box-counting statistics for the authentication of Pollock's dripped works, particularly if such measures are used in conjunction with multiple features, machine learning and art material studies and connoisseurship.

Paper Details

Date Published: 2 February 2009
PDF: 11 pages
Proc. SPIE 7251, Image Processing: Machine Vision Applications II, 72510Q (2 February 2009); doi: 10.1117/12.806245
Show Author Affiliations
Mohammad Irfan, Stony Brook Univ. (United States)
David G. Stork, Ricoh Innovations, Inc. (United States)
Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 7251:
Image Processing: Machine Vision Applications II
Kurt S. Niel; David Fofi, Editor(s)

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