Abstract
In this paper, a painting genre classification system is proposed. Four feature descriptors about the color and texture defined in the MPEG-7 specification, which are more against painting characteristics, are extracted from data sets. Then, we use a self-adaptive harmony search algorithm to select relevant features (or a local feature set) to train each one-against-one SVM classifier. Finally, a majority voting strategy on N(N-1)/2 prediction results would determine their respective genres of paintings. The experimental results show that the overall accuracy reaches 69.8%, and this demonstrates more precise features can be selected for each pair of genres to get better classification results.
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References
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Culjak, M., Mikuš, B., Jež, K., Hadjic, S.: Classification of art paintings by genre. In: Pro. the 34th International Convention on Information and Communication Technology, Electronics and Microelectronics, pp. 1634–1639 (2011)
Deriche, M.: Feature selection using ant colony optimization. In: Proc. the 6th International Multi-Conference on Systems, Signals and Devices, pp. 1–4 (2009)
Diao, R., Shen, Q.: Two new approaches to feature selection with harmony search. In: Proc. the IEEE International Conference on Fuzzy Systems, pp. 1–7 (2010)
Huang, Y.-F., Wang, C.-M.: Self-adaptive harmony search algorithm for optimization. Expert Systems with Applications 37(4), 2826–2837 (2010)
Jin, X., Bie, R.: Random forest and PCA for self-organizing maps based automatic music genre discrimination. In: Proc. the International Conference on Data Mining, pp. 414–417 (2006)
Lanzi, P.L.: Fast feature selection with genetic algorithms: a filter approach. In: Proc. the IEEE International Conference on Evolutionary Computation, pp. 537–540 (1997)
Martinez, J.M., Koenen, R., Pereira, F.: MPEG-7: the generic multimedia content description standard, part 1. IEEE Multimedia 9(2), 78–87 (2002)
Mitra, V., Wang, C.J.: A neural network based audio content classification. In: Proc. the International Joint Conference on Neural Networks, pp. 1494–1499 (2007)
Mokadem, A., Charbit, M., Chollet, G., Bailly, K.: Age regression based on local image features. In: Proc. the 4th Pacific-Rim Symposium on Image and Video Technology, pp. 88–93 (2010)
Silviu, G.: Information Theory with Applications. McGraw-Hill (1977)
Zujovic, J., Gandy, L., Friedman, S., Pardo, B., Pappas, T.N.: Classifying paintings by artistic genre: an analysis of features & classifiers. In: Proc. the IEEE International Workshop on Multimedia Signal Processing, pp. 1–5 (2009)
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Huang, YF., Wang, CT. (2014). Classification of Painting Genres Based on Feature Selection. In: Park, J., Chen, SC., Gil, JM., Yen, N. (eds) Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54900-7_23
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DOI: https://doi.org/10.1007/978-3-642-54900-7_23
Publisher Name: Springer, Berlin, Heidelberg
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