Abstract
In assessing the authenticity of art work it is of high importance from the art expert point of view to understand the reasoning behind it. While complex data mining tools accompanied by large feature sets extracted from the images can bring accuracy in paintings authentication, it is very difficult or not possible to understand their underlying logic. A small feature set linked to a minor classification error seems to be the key to understanding and interpreting the obtained results. In this study the selection of a small feature set for painting classification is done by the means of building an optimal pruned decision tree. The classification accuracy and the possibility of extracting knowledge for this method are analyzed. The results show that a simple small interpretable feature set can be selected by building an optimal pruned decision tree.
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Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Transactions Systems, Man and Cybernetics SMC-3, 610–621 (1973)
van den Herik, H.J., Postma, E.O.: Discovering the visual signature of painters. In: Future Directions for intelligent Systems and Information Sciences, pp. 129–147. Physica Verlag, Heidelberg (2000)
Osei-Bryson, K.M.: Evaluation of decision trees: a multi-criteria approach. Computers & Operation Research 31, 1933–1945 (2004)
Sablatnig, R., Kammerer, P., Zolda, E.: Hierarchical Classification of Paintings using Face-and Brush Stroke Models. In: 14th International Conference on Pattern Recognition, vol. 1, pp. 172–174 (1998)
Lyu, S., Rockmore, D., Farid, H.: A digital technique for art authentication. Proceedings of the National Academy of Sciences 101, 17006–17010 (2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Deac, A.I., van der Lubbe, J., Backer, E. (2006). Feature Selection for Paintings Classification by Optimal Tree Pruning. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds) Multimedia Content Representation, Classification and Security. MRCS 2006. Lecture Notes in Computer Science, vol 4105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11848035_47
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DOI: https://doi.org/10.1007/11848035_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39392-4
Online ISBN: 978-3-540-39393-1
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