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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.
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