Quantification of artistic style through sparse coding analysis in the drawings of Pieter Bruegel the Elder
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- James M. Hughes
- Departments of Computer Science and
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- Daniel J. Graham
- Mathematics, Dartmouth College, Hanover, NH 03755; and
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- Daniel N. Rockmore
- Departments of Computer Science and
Description
<jats:p>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.</jats:p>
Journal
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 107 (4), 1279-1283, 2010-01-05
Proceedings of the National Academy of Sciences
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Details 詳細情報について
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- CRID
- 1362825894183527552
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- ISSN
- 10916490
- 00278424
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- Data Source
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- Crossref