Fast and accurate image classification with histogram based features and additive kernel SVM

Kernel-based image classification methods rely on the considered kernel functions that can be chosen with respect to prior information on the adopted features. In remote sensing, histogram features have recently gained an increasing interest due to their capability to address several critical classification problems (e.g., the problem of curse of dimensionality) when appropriate kernels and classifiers are selected. In view of that, in this paper we introduce in remote sensing additive kernels in the context of support vector machine classification (AK-SVM), which are suitable kernels for histogram based feature representations. In particular, we investigate the Histogram Intersection kernel and the chi-square kernel within the AK-SVM. Moreover, we present fast implementations of the AK-SVM to significantly speed up the classification phase of the SVM. Experimental results show the effectiveness of the AK-SVM in terms of classification accuracy and computational time when compared to SVMs with standard kernels.