SVM Compound Kernel Functions for Vehicle Target Classification

Abstract

The focus of this paper is to explore the use of kernel combinations of the support vector machines (SVMs) for vehicle classification. Being the primary component of the SVM, the kernel functions are responsible for the pattern analysis of the vehicle dataset and to bridge its linear and non-linear features. However, the choice of the type of kernel functions has characteristics and limitations that are highly dependent on the parameters. Thus, in order to overcome these limitations, a method of compounding kernel function for vehicle classification is hereby introduced and discussed. The vehicle classification accuracy of the compound kernel function presented is then compared to the accuracies of the conventional classifications obtained from the four commonly used individual kernel functions (linear, quadratic, cubic, and Gaussian functions). This study provides the following contributions: (1) The classification method is able to determine the rank in terms of accuracies of the four individual kernel functions; (2) The method is able to combine the top three individual kernel functions; and (3) The best combination of the compound kernel functions can be determined.