Abstract: Traditional Chinese painting(TCP) and calligraphy is unique forms of art. With the rapid development of digi tal technology,more and more TC;P and Calligraphy works are digitized. How to effectively retrieve these images be- comes a hot topic. If we first classify the TCP and Calligraphy images, this will be a solid foundation for retrievaling those images. We proposed an improved classification method of those images.‘I_iubai' area was detected firstly, and removed it from the images,because these regions contain noise information which will make the classifation results in- accurate. The second step was to extract feature from those images. At last, the features were used to training the Sup- port Vector Machine(SVM) model. And the trained model was used to classifying the TCP and Calligraphy images. The classification result shows this method has better effect.

Key words: Chinese painting images, Chinese calligraphy images, SVM, Classifation