a procedure of adaptive kernel combination with kernel-target alignment for object classification
In order to achieve good performance in object classification problems, it is necessary to combine information from various image features. Because the large margin classifiers are constructed based on similarity measures between samples called kernels, finding appropriate feature combinations boils down to designing good kernels among a set of candidates, for example, positive mixtures of predetermined base kernels. There are a couple of ways to determine the mixing weights of multiple kernels: (a) uniform weights, (b) a brute force search over a validation set and (c) multiple kernel learning (MKL). MKL is theoretically and technically very attractive, because it learns the kernel weights and the classifier simultaneously based on the margin criterion. However, we often observe that the support vector machine (SVM) with the average kernel works at least as good as MKL. In this paper, we propose as an alternative, a two-step approach: at first, the kernel weights are determined by optimizing the kernel-target alignment score and then the combined kernel is used by the standard SVM with a single kernel. The experimental results with the VOC 2008 data set [8] show that our simple procedure outperforms the average kernel and MKL.
kernel-target alignment object classification multiple kernel learning