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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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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