JACIII Vol.22 No.5 pp. 654-659
doi: 10.20965/jaciii.2018.p0654


SVM Compound Kernel Functions for Vehicle Target Classification

Edison A. Roxas, Ryan Rhay P. Vicerra, Laurence A. Gan Lim, Elmer P. Dadios, and Argel A. Bandala

Gokongwei College of Engineering, De La Salle University
106 Miguel Building, 2401 Taft Avenue, Malate, Manila 1004, Philippines

March 6, 2018
June 8, 2018
September 20, 2018
computer vision, traffic monitoring and surveillance, vehicle classification, support vector machine, compound kernel function
SVM Compound Kernel Functions for Vehicle Target Classification

Compound kernel model and results

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.

Cite this article as:
E. Roxas, R. Vicerra, L. Lim, E. Dadios, and A. Bandala, “SVM Compound Kernel Functions for Vehicle Target Classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.5, pp. 654-659, 2018.
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Last updated on Oct. 23, 2018