A SVM Multi-Class Image Classification Method Based on de and KNN in Smart City Management

Abstract

When directly operated in an image, good results are always difficult to achieve via conventional methods because they have poor high-dimensional performance. Support vector machine (SVM) is a type of machine learning method with solid foundation that is developed based on traditional statistics. It is also a theory for statistical estimation and predictive learning of objects. This paper optimizes the structure of SVM classification tree with differential evolution (DE) and designs the corresponding DE algorithm to effectively solve the problem of image classification of complex background cases in smart city management systems. In the training process of SVM classification tree, it obtains an optimal two-class classification scheme in every node by means of DE, initially separates the classes that are easy to be separated and then the less easy ones, and finally adaptively generates the best classification tree. The simulation experiment proves that the proposed algorithm is effective when applied to smart city management systems.