Supervised Learning-Based Feature Selection for Mondrian Paintings Style Authentication
Keon Myung Lee*, Kyoung Soon Hwang*, Kyung Mi Lee*,
Seung Kee Han**, Woo Hyun Jung***, and Seungbok Lee***
*Department of Computer Science, Chungbuk National University, Cheongju, Chungbuk 361-763, Korea
**Department of Physics, Chungbuk National University, Cheongju, Chungbuk 361-763, Korea
***Department of Psychology, Chungbuk National University, Cheongju, Chungbuk 361-763, Korea
This paper concerns feature selection for computational analysis in authenticating works of art. The various features designed and extracted from art work in art forgery detection or the identification of the characteristics of art work style are valuable only when they have a meaningful influence on a given task such as classification. This paper presents features applicable to authenticating the painting style of Piet Mondrian and demonstrates meaningful features by using two supervised learning algorithms, a decision tree induction algorithm C4.5 and the Feature Generating Machine (FGM), both of which are used to select important features in the course of learning.
Seung Kee Han, Woo Hyun Jung, and Seungbok Lee, “Supervised Learning-Based Feature Selection for Mondrian Paintings Style Authentication,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.7, pp. 894-899, 2012.
-  G. Polatkan, S. Jafarpour, A. Brasoveanu, S. Hughes, and I. Daubechies, “Detection of forgery in paintings using supervised learning,” Proc. of IEEE Int. Conf. on Image Processing (ICIP2009), pp. 2921-2924, 2009.
-  S. Lyu, D. Rockmore, and H. Farid, “A digital technique for art authentication,” Proc. of the National Academy of Sciences, Vol.101, No.49, pp. 17006-17010, 2004.
-  R. Jacobsen and M. Nielsen, “Robustness of digital artist authentication,” Research Report Series, No.R-2011-12, Department of Mathematical Sciences, Aalborg University, 2011.
-  D. Andrzejewski, D. G. Stork, X. Zhu, and R. Spronk, “Inferring compositional style in the neo-plastic paintings of Piet Mondrian by machine learning,” Electronic Imaging: Computer Image Analysis in the Study of Art (SPIE 2010), 2010.
-  J. Y. Shen and T. Gedeon, “Cyber-Genetic Neo-Plasticism – An AI program creating Mondrian-like paintings by using interactive bacterial evolution algorithm,” 2007.
-  P. Locher, K. Overbeeke, and P. J. Stappers, “Spatial balance of color triads in the abstract art of PietMondrian,” Perception, Vol.34, pp. 169-189, 2005.
-  T. Schnier and J. S. Gero, “From Frank Lloyd Wright to Mondrian: Transforming evolving representations,” Adaptive Computing in Design and Manufacture, I. Parmee (Ed.), pp. 207-219, Springer, 2009.
-  J. Chen, “Reproducing Paintings with Mondrians style during the Period of 1921-1933,” J. of Nat. Taipei Teachers College, Vol.17, No.2, pp. 105-120, 2004.
-  B. Pham, “Design for aesthetics: interactions of design variables and aesthetic properties,” Proc. of SPIE IS&T/SPIE 11th Symposium, Vol.3644, pp. 364-371, 1999.
-  C. M. Bishop, “Pattern recognition and Machine learning,” Springer, 2006.
-  S. Theodoridis and K. Koutroumbas, “Pattern Recognition,” Academic Press, 2009.
-  Y. Sun and D. Wu, “A RELIEF based feature extraction algorithm,” Proc. of SIAM Int. Conf. on Data Mining 2008, pp. 188-195, 2008.
-  D. Uhm, S. Jun, and S. J. Lee, “A classification method using data reduction,” Int. J. of Fuzzy Logic and Intelligent Systems, Vol.12, No.1, pp. 1-5, 2012.
-  M. Tan, L. Wang, and I. W. Tsang, “Learning sparse SVM for feature selection on very high dimensional datasets,” Proc. of the 27th Int. Conf. on Machine Learning (ICML 2010), Israel, June 2010.
-  M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software: an update,” SIGKDD Explorations, Vol.11, Issue 1, 2009.
-  M. Tan, I. Tsang, and L. Wang, “Feature generating machine version: 1.0,” 2011.
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