新的纹理图像特征提取方法  

Novel approach for feature extraction of texture images

作　　者：刘明霞[1] 侯迎坤[1,2] 郭小春[1] 张燕[3] 杨德运[1] 

机构地区：[1]泰山学院信息科学技术学院,山东泰安271021 [2]南京理工大学计算机科学与技术学院,南京210094 [3]泰安市岱岳区环境保护局,山东泰安271000

出　　处：《计算机应用》2009年第12期3434-3436,共3页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60872161);泰山学院科研基金计划项目(Y06-2-16)

摘　　要：提出了一种新的基于非下采样Contourlet变换的纹理特征提取方法。首先对纹理图像进行非下采样Contourlet变换,然后提取不同尺度、不同方向上变换系数矩阵的均值和方差作为特征向量,大大降低了特征维数,并利用BP神经网络进行训练和仿真,实现了纹理图像的自动分类。实验结果表明,与小波包变换和改进的LBP纹理算子等方法相比,该方法能取得更好的分类效果。A novel approach for feature extraction of texture images based on NonSubsampled Contourlet Transform （NSCT） was proposed. The coefficients in different scales and different directions were obtained by textural image decomposition using NSCT. Then the means and variances of theses coefficients were extracted to be the feature vectors, which could greatly reduce the number of feature dimension. Back Propagation （BP） neural network was adopted to implement automatic classification of texture images through training and simulation. Compared with wavelet package transform and the improved Local Binary Pattern （LBP） texture descriptor, this approach can achieve better result.

关 键 词：非下采样CONTOURLET变换 纹理分类 小波包变换 局部二值模式 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]