二元树复小波在图像特征提取和分类中的设计与应用  

Design and Application of Image Feature Extraction and Classification by Using Dual-Tree Complex Wavelet Transform

作　　者：李政[1] 

机构地区：[1]山东艺术学院现代技术教育部,山东济南250014

出　　处：《云南民族大学学报（自然科学版）》2011年第1期53-57,共5页Journal of Yunnan Minzu University:Natural Sciences Edition

基　　金：山东省研究生教育创新计划重点项目(SDYZ06001)

摘　　要：通过二元树复小波变换对图像进行4尺度分解,提取每一尺度下代表6个方向的高频带子图小波系数模的均值和标准方差组成48维的特征向量,利用支持向量机的一对一多分类算法对Brodatz图像库中的112幅图像进行了纹理特征提取和分类实验,结果表明二元树复小波变换提取的图像特征能有效提高图像的分类精度.The dual - tree complex wavelet transform was used to decompose images with four scales, and the 48 - dimensional feature vector was generated by computing mean and standard deviation from wavelet coefficients of six - direction high - frequency subbands of each scale, using one - vs - one algorithm of the support vector machine, the texture feature extraction and classification experiments were done by using 112 images of Brodatz image database. The results show that the image features of the dual - tree complex wavelet transform can effectively improve the image classification accuracy.

关 键 词：二元树复小波变换 小波系数 支持向量机 特征提取 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]