Support Vector Machines (SVMs) deliver stateof-the-art performance in real-world applications and are now established as one of the standard tools for machine learning and data mining. A key problem of these methods is how to choose an optimal kernel and how to optimise its parameters. The real-world applications have also emphasised the need to consider a combination of kernelsa multiple kernel-in order to boost the classification accuracy by adapting the kernel to the characteristics of heterogeneous data. This combination could be linear or non-linear, weighted or un-weighted. Several approaches have been already proposed to find a linear weighted kernel combination and to optimise its parameters together with the SVM parameters, but no approach has tried to optimise a non-linear weighted combination. Therefore, our goal is to automatically generate and adapt a kernel combination (linear or nonlinear, weighted or un-weighted, according to the data) and to optimise both the kernel parameters and SVM parameters by evolutionary means in a unified framework. We will denote our combination as a kernel of kernels (KoK). Numerical experiments show that the SVM algorithm, involving the evolutionary kernel of kernels (eKoK) we propose, performs better than well-known classic kernels whose parameters were optimised and a state of the art convex linear and an evolutionary linear, respectively, kernel combinations. These results emphasise the fact that the SVM algorithm could require a non-linear weighted combination of kernels.
In this paper different matching cost functions used for stereo matching are evaluated in the context of intelligent vehicles applications. Classical costs are considered, like: sum of squared differences, normalized cross correlation or census transform that were already evaluated in previous studies, together with some recent functions that try to enhance the discriminative power of Census Transform (CT). These are evaluated with two different stereo matching algorithms: a global method based on graph cuts and a fast local one based on cross aggregation regions. Furthermore we propose a new cost function that combines the CT and alternatively a variant of CT called Cross-Comparison Census (CCC), with the mean sum of relative pixel intensity differences (DIFFCensus). Among all the tested cost functions, under the same constraints, the proposed DIFFCensus produces the lower error rate on the KITTI road scenes dataset 1 with both global and local stereo matching algorithms.
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