Dr. Mohammad Tanvir Irfan Profile

Dr. Mohammad Tanvir Irfan

Grad Student/TA at Bowdoin College

SPIE Involvement:

Author

Publications (2)

Proceedings Article | 11 March 2011 Paper

Boosting multi-feature visual texture classifiers for the authentication of Jackson Pollock's drip paintings

Mahmoud Al-Ayyoub, Mohammad Irfan, David Stork

Proceedings Volume 7869, 78690H (2011) https://doi.org/10.1117/12.873142

KEYWORDS: Fractal analysis, Visualization, Error analysis, Image analysis, Visual analytics, Image resolution, Pattern recognition, Neodymium, Feature extraction, Feature selection

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Proceedings Article | 3 February 2009 Paper

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

Mohammad Irfan, David Stork

Proceedings Volume 7251, 72510Q (2009) https://doi.org/10.1117/12.806245

KEYWORDS: Fractal analysis, Visualization, Image classification, Image analysis, Image segmentation, Pattern recognition, Feature extraction, Machine learning, Machine vision, Wavelets

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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.

Conference Committee Involvement (2)

Computer Vision and Image Analysis of Art II

26 January 2011 | San Francisco Airport, California, United States

Computer Vision and Image Analysis of Art

18 January 2010 | San Jose, California, United States

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