Semi supervised deep kernel design for image annotation

It is commonly agreed that the success of support vector machines (SVMs), is highly dependent on the choice of particular similarity functions referred to as kernels. The latter are usually handcrafted or designed using appropriate optimization schemes. Multiple kernel learning (MKL) is one possible scheme that designs kernels as sparse or convex linear combinations of existing elementary functions. However, this results into shallow kernels, which are powerless to capture the right similarity between data, especially when content of these data is highly semantic. In this paper, we redefine multiple kernels using a deep architecture. In this new formulation, a global kernel is learned as a multi-layered linear combination of activation functions, each one involves a combination of several elementary or intermediate functions on multiple features. We propose three different settings to learn the weights of these kernel combinations; supervised, unsupervised and semi-supervised. When plugged into SVMs, the resulting deep multiple kernels show a gain, compared to shallow kernels, for the challenging task of image annotation using the ImageCLEF benchmark.