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马永军,方凯,方廷健(中国科学技术大学自动化系,合肥 230027;中科院合肥智能机械研究所,合肥 230031)

摘 要
Classification Based on Support Vector Machine and Distance Classification for Texture Image

MA Yong jun,FANG Kai,FANG Ting jian()

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.