用于不同尺寸图像分类的平移不变小波特征提取
Extraction of shift invariant wavelet features for classification of images with different sizes.
作者信息
Pun Chi-Man, Lee Moon-Chuen
机构信息
Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, SAR.
出版信息
IEEE Trans Pattern Anal Mach Intell. 2004 Sep;26(9):1228-33. doi: 10.1109/TPAMI.2004.67.
An effective shift invariant wavelet feature extraction method for classification of images with different sizes is proposed. The feature extraction process involves a normalization followed by an adaptive shift invariant wavelet packet transform. An energy signature is computed for each subband of these invariant wavelet coefficients. A reduced subset of energy signatures is selected as the feature vector for classification of images with different sizes. Experimental results show that the proposed method can achieve high classification accuracy of 98.5 percent and outperforms the other two image classification methods.
提出了一种有效的用于不同尺寸图像分类的平移不变小波特征提取方法。特征提取过程包括归一化，然后是自适应平移不变小波包变换。为这些不变小波系数的每个子带计算能量特征。选择能量特征的一个缩减子集作为用于不同尺寸图像分类的特征向量。实验结果表明，所提出的方法能够实现98.5%的高分类准确率，并且优于其他两种图像分类方法。
Zotero 插件