基于小波变换的国画特征提取及分类

Automatic Categorization of Traditional Chinese Paintings Based on Wavelet Transform

像素域内利用图像处理技术对图像进行特征提取得到广泛研究。为了在新的信号域内找到更好的图像特征表示方法,提出在小波域内利用不同分辨率及频带的图像结构所展现的艺术风格的不同表现形式来获得国画艺术深度信息的方法。该方法利用三层小波变换提取图像的纹理特征,并采用3种不同的分类器决策树、BP神经网络和支持向量机,对不同画家的风格进行学习,以完成自动分类。实验结果表明,该算法能有效提取图像纹理特征,实现国画的自动分类。

Image processing based feature extraction is widely studied in pixel domain. In order to find a better method of image feature representation in the new signal domain, a number of new artistic features were proposed to exploit the advantage of decomposition from the input art works and characterize the artistic styles across different sub-bands in wavelet domain. In order to achieve automatic categorization, 3-layer wavelet transform was employed for the extraction of images＇ texture features. Moreover, three different classifiers were compared and used to learn different artistic style. Experimental results show that the algorithm can effectively extract image texture features and achieve high accu- racy of classification.