Speeding up Support Vector Machine (SVM) image classification by a kernel series expansion

Abstract

Due to their flexibility, and capacity to handle high dimensional vectorial data, support vector machines (SVMs) have become the reference for remote sensing imagery classification. However when processing large amounts of data the SVM classification could be a time consuming process. In this paper a new decomposition scheme of the SVM decision function is proposed. The decomposition is based on using the Taylor series expansion to approximate the kernel function. Then, using the results of the optimization problem of the SVM after the learning phase, this expansion is used to obtain an approximate decision function that provides a trade-off between the classification accuracy and the processing time. This speeds-up the SVM classification if limited processing time is available and favors accuracy if sufficient processing time is available.