基于支持向量机和距离度量的纹理分类

针对图象纹理分类问题，提出了一种将支持向量机和距离度量相结合，以构成两级组合分类器的分类方法，用该方法分类时，先采用距离度量进行前级分类，然后根据图象的纹理统计特征，采用欧氏距离来度量图象之间的相似性，若符合条件，则给出分类结果，否则拒识，并转入后级分类器，而后级分类器则采用一种新的模式分类方法－支持向量机进行分类，该组合分类方法不仅充分利用了支持向量机识别率高和距离度量速度快的优点，并且还利用距离度量的结果去指导支持向量机的训练和测试，由纹理图象分类的实验表明，该算法具有较高的效率和识别精度，同时也对推动支持向量机这一新的模式分类方法的实际应用具有积极意义。

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

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.