Oil Painting Classification

By Shiyu Luo

Dec. 2010

OutlineMotivation and GoalMethodsFeature extractionsMLPClassification ResultsAnalysis and conclusionReferences

Motivation and GoalOil paintings are of great value

Art History

Even more counterfeits make it harder to identify the authentic worksTraditional: signatures, Dates and producers of

canvas, etc.Proposal: by Digital Image Processing

Brushwork example of one of da Vinci’s paintingLeft: Brushwork in original painting

Right: micro-view of grey-degree of the red square

Cont’dIn this pilot project, painting-based approaches are

studiedData set: 8 X-rayed paintings from Leonardo da Vinci

Method:Patch selectionFeature extractionMulti Layer Perceptron

Feature extractionGeneral requirements:

Intra-class variance must be smallInter-class separation should be largeIndependent of the size, orientation, and location of the

patternFour 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

Multi Layer Perceptron (MLP)MLP: Error Back Propagation

A diagram demonstration of Multi Layer Perceptron

Result

Analysis & ConclusionGenerally 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.

Future work and improvementX-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

Selected ReferencesSiwei 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. 2nd edition. Prentice-Hall. 2002.