Transductive kernel map learning and its application to image annotation

Abstract

We introduce in this paper a novel image annotation approach based on maximum margin classification and a new class of kernels. The method goes beyond the naive use of existing kernels and their restricted combinations in order to design "model-free" transductive kernels applicable to interconnected image databases. The main contribution of our method includes the minimization of an energy function mixing i) a reconstruction term that factorizes a matrix of interconnected image data as a product of a learned dictionary and a learned kernel map ii) a fidelity term that ensures consistent label predictions with those provided in a training set and iii) a smoothness term which guarantees similar labels for neighboring data and allows us to iteratively diffuse kernel maps and labels from labeled to unlabeled images. Solving this minimization problem makes it possible to learn both a decision criterion and a kernel map that guarantee linear separability in a high dimensional space and good generalization performance. Experiments conducted on image annotation, show that our obtained kernel achieves at least comparable results with related state of the art methods on the MSRC and the Corel5k databases.