Deep kernel map networks for image annotation

Abstract

Deep multiple kernel learning is a powerful technique that selects and deeply combines multiple elementary kernels in order to provide the best performance on a given classification task. This technique, particularly effective, becomes intractable when handling large scale datasets; indeed, multiple nonlinear kernel combinations are time and memory demanding., In this paper, we propose a new framework that significantly reduces the complexity of deep multiple kernels. Given a deep kernel network (DKN), our method designs its equivalent deep map network (DMN), using multi-layer explicit maps that approximate the initial DKN with a high precision. When combined with support vector machines, the design of DMN preserves high classification accuracy compared to its underlying DKN while being (at least) an order of magnitude faster. Experiments conducted on the challenging Im-ageCLEF2013 annotation benchmark, show that the proposed DMN is indeed effective and highly efficient.