Classification Based on Support Vector Machine and Distance Classification for Texture Image
基于支持向量机和距离度量的纹理分类

Support vector machine(SVM) is a novel type of learning machine, this thesis introduces the theory of SVM briefly and application in a classification system for texture image, and discusses in detail the core techniques and algorithms, which combine SVM and distance classification into two layer serial classifier. SVM has shown to provide better generalization performance than traditional techniques. However, because using Quadratic Programming (QP) optimization techniques, the training of SVM is time consuming, especially when the training data set is very large. So we have two classifiers combined. Firstly, a rejecting coefficient and rejecting rule are defined. According the rejecting rule, the distance classifier can classify the images and give the final results, or reject to classify the input images. The rejected images are fed into SVM for further classification. The algorithms can take advantages of SVM and distance classification. The experiments show that the algorithms have low error rate and high speed.