In this paper we formulate multiple kernel learning (MKL) as a distance metric learning (DML) problem. More specifically, we learn a linear combination of a set of base kernels by optimising two objective functions that are commonly used in distance metric learning. We first propose a global version of such an MKL via DML scheme, then a localised version. We argue that the localised version not only yields better performance than the global version, but also fits naturally into the framework of example based retrieval and relevance feedback. Finally the usefulness of the proposed schemes are verified through experiments on two image retrieval datasets. © 2011 Springer-Verlag.
CITATION STYLE
Yan, F., Mikolajczyk, K., & Kittler, J. (2011). Multiple kernel learning via distance metric learning for interactive image retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6713 LNCS, pp. 147–156). https://doi.org/10.1007/978-3-642-21557-5_17
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