1. By Shiyu Luo 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., 2 nd 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 2 nd 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 Hany Farid. 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. 2 nd edition. Prentice-Hall. 2002.