TEXTURE CLASSIFICATION BY WAVELET TRANSFORM
基于子波变换的纹理图像分类

This paper describes the characterization of texture properties at multiple scales and orientations using the wavelet transform, and introduces a new wavelet feature suitable for textured image classification. It is pointed out that the new feature is superior to conventional energy measurement by analyzing its stability and its visual proterty in detail. Finally, nine kinds of natural images are classified successfully based on wavelet feature using BP neural network. The results demonstrate natural textured images can be classified without error and done at higher correct classification rate under white noise.