Application of PSO and SVM in image classification

In the few years, several neural networks are proposed to image classification. Support vector machine classifier employs the structural risk minimization principles, which make support vector machine classifier have good generalization ability. In order to solve the problem of parameters selection of support vector machine, particle swarm optimization is applied to select the parameters of support vector machine. Therefore, support vector machine trained by particle swarm optimization is presented to image classification in the paper. The images in Corel image database are used to testify the classification performance of the proposed method. The testing results show that the classification accuracy of PSO-SVM is better than that of SVM,BP neural network, RBF neural network.