纹理特征作为图像的一个重要特征，在国画分类识别中的地位十分重要，但现有的纹理提取算法大多基于灰度信息而忽略了颜色信息。针对国画分类识别中纹理提取算法存在的问题，本文提出了一种多尺度、多色域的纹理特征提取算法，该算法结合了轮廓波变换和灰度共生矩阵的优点。为了对国画进行特征提取，该算法首先将国画图像转变到HSI 色彩空间。然后，提取色调、饱和度、强度这三个色彩分量进行分区域操作，即提取每一个色彩分量的纹理特征。最后，将提取的3个特征向量融合并进行主成分分析降维。实验证明，与灰度共生矩阵相比，本文算法在国画分类识别方面查准率提高了7．5％，查全率提高了8．7％。实验表明多尺度灰度共生矩阵算法优于传统的灰度共生矩阵算法。
As an important feature,texture feature is very important in the category of Chinese paint-ing,but the majority of the existing texture extraction algorithms is based on gray-scale information. To solve the problems in Chinese painting texture extraction classification,a multi-scale,multi-color domain texture feature extraction algorithm has been proposed.This algorithm combines the advanta-ges of Contourlet transform and Gray-level Co-occurrence Matrix.In order to extract Chinese painting features by the new algorithm,the image is first transformed into HSI color space.Then,the three color components of HSI is extracted to sub-regional operation,which is to extract the texture charac-teristics of each color component.Finally,three feature vectors are integrated and the dimensionality of matrix is reduced using Principal Component Analysis.Experimental results show that compared with Gray-level Co-occurrence Matrix,the algorithm improves the precision of 7.5%,re-check rate increased by 8.7% in terms of Chinese painting classification.The experiment show that the new algo-rithm presented in this paper is better than Gray-level Co-occurrence Matrix algorithm.
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