基于Gabor理论的山水画皴法分类

Abstract

提取具有代表性的特征进行纹理描述和分类一直是纹理分析的热点。本文针对不同皴法的国画，运用了一种Gabor滤波器技术进行分类：通过纹理特征提取，利用几何归一化和光线归一化方法将国画图像进行预处理，再对Gabor滤波器组滤波后组成的高维特征矢量通过主成分分析(PCA)进行降维，最后采用支持向量机(SVM)方法进行纹理分类。这种分类方法的准确率可达95.5%。 Extracting the effective features for texture description and classification has always been the hot spot of the texture analysis. In this paper, according to different texture of traditional Chinese painting, we use a kind of Gabor filter technique to classify the painting. By texture feature extraction, first of all, we preprocess the traditional Chinese painting images with geometric normalization and light normalization, after that we process the group of the Gabor filter of high dimensional feature vectors by principal component analysis (PCA) for dimension reduction. Finally, support vector machine (SVM) method is employed for texture classification. The accuracy rate of this classification method can reach 95.5%.