Shape and texture-based image classification using wavelet
利用小波进行基于形状和纹理的图像分类

This paper presented a method of image classification using wavelet based on shape and texture. Firstly, to use wavelet to transform the images and get edge images, then extract seven edge invariant moments as shape feature vectors. In experiments, finding that the background of images disturb the accuracy of classification in most cases, so to wipe off the background of images and account five second statistics as texture features on the basis of Gray Level Co-occurrence matrix. Lastly, to combine shape features and texture features as image feature vectors, and normalize them by Gauss method, and classify the images by SVM. Experimental results verify the superiority and strong practicability of this method.