Jiulong Zhang
ABSTRACT
Ancient Chinese calligraphic works have important historic and artistic values, and styles classification based on image pattern recognition is a significant issue to be addressed. To attack the drawbacks of some conventional image operators in this scenario, a method based on the fusion of GIST and Pyramid Histogram of Oriented Gradients(PHOG) feature is proposed. This method uses GIST and PHOG to extract the global features and the local contour features respectively, then feed the two features to PCA for dimensionality reduction, and connect the reduced features after PCA to form a fusion feature. Finally, the Support Vector Machine(SVM) classifier performs the style training and classification. The algorithm is verified on datasets of four tablets by calligrapher Yan Zhenqing in different periods; both the classification rates on single features and on fused features, as well as the influence of numbers of training samples on classification performance demonstrates the effectiveness of the proposed method.
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Index Terms
- Combination of GIST and PHOG Features for Calligraphy Styles Classification
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