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Oil Painting Classification

Oil Painting Classification. By Shiyu Luo Dec. 2010. Outline. Motivation and Goal Methods Feature extractions MLP Classification Results Analysis and conclusion References . Motivation and Goal. Oil paintings are of great value Art History

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Oil Painting Classification

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  1. Oil Painting Classification By ShiyuLuo Dec. 2010

  2. Outline • Motivation and Goal • Methods • Feature extractions • MLP • Classification Results • Analysis and conclusion • References

  3. Motivation and Goal • Oil paintings are of great value • Art • History • Even more counterfeits make it harder to identify the authentic works • Traditional: signatures, Dates and producers of canvas, etc. • Proposal: by Digital Image Processing

  4. Brushwork example of one of da Vinci’s painting Left: Brushwork in original painting Right: micro-view of grey-degree of the red square

  5. Cont’d • In this pilot project, painting-based approaches are studied • Data set: 8 X-rayed paintings from Leonardo da Vinci • Method: • Patch selection • Feature extraction • Multi Layer Perceptron

  6. Feature extraction • General requirements: • Intra-class variance must be small • Inter-class separation should be large • Independent of the size, orientation, and location of the pattern • Four features are employed • Fourier Transform (Brushworks) • Wavelet Transform (lower resolution image) • Statistical Approach (texture) • E.g., 2nd moment: a measure of gray-level contrast to describe relative smoothness • Covariance Matrix

  7. Multi Layer Perceptron (MLP) • MLP: Error Back Propagation A diagram demonstration of Multi Layer Perceptron

  8. Result

  9. Analysis & Conclusion • Generally speaking, C_rate can be achieved at around 40% - 50% • 50x50 patch-based generally achieves better and more stable results than 100x100 patch-based does. • For 50x50 patch-based, the better and relatively stable results are those with 6-8 neurons in hidden layer. • Those “excellent” results of 100x100 maybe I’m “luck” in the 3 trails.

  10. Future work and improvement • X-rays maybe one of the limits on achieving better classification rates; colored paintings could be used in the future • 2nd or higher order wavelet transforms maybe used to improve the feature vector • Other neuron network methods are to be tested to better suit this painting classification problem

  11. Selected References • Siwei Lyn, Daniel Rockmore, and HanyFarid. A digital technique for art authentication. 17006-17010, PNAS, Dec. 2004, vol. 101, no.49. • C. Richard Johnson, Jr., Ella Hendriks, Igor J. Berezhnoy, Eugene Brevdo, Shannon M. Hughes, Ingrid Daubechies, Jia Li, Eric Postma, and James Z. Wang. Image Processing for Artist Identification: Computerized Analysis of Vincent van Gogh’s Painting Brushstrokes. • Jana Zujovic, Scott Friedman, Lisa Gandy, Identifying painting genre using neural networks. Northwestern University. • G. Y. Chen and B. Kegl. Feature Extraction Using Radon, Wavelet and Fourier Transform. Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on, pp. 1020-1025. Oct. 2007. • Rafael C. Gonzalez, Richard E. Woods. Digital Image Processing. 2nd edition. Prentice-Hall. 2002.

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