A New Multiple Kernel Approach for Visual Concept Learning

In this paper, we present a novel multiple kernel method to learn the optimal classification function for visual concept. Although many carefully designed kernels have been proposed in the literature to measure the visual similarity, few works have been done on how these kernels really affect the learning performance. We propose a Per-Sample Based Multiple Kernel Learning method (PS-MKL) to investigate the discriminative power of each training sample in different basic kernel spaces. The optimal, sample-specific kernel is learned as a linear combination of a set of basic kernels, which leads to a convex optimization problem with a unique global optimum. As illustrated in the experiments on the Caltech 101 and the Wikipedia MM dataset, the proposed PS-MKL outperforms the traditional Multiple Kernel Learning methods (MKL) and achieves comparable results with the state-of-the-art methods of learning visual concepts.