Multiple Kernel Learning via Distance Metric Learning for Interactive Image Retrieval

  • Fei Yan
  • Krystian Mikolajczyk
  • Josef Kittler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fei Yan
    • 1
  • Krystian Mikolajczyk
    • 1
  • Josef Kittler
    • 1
  1. 1.Centre for Vision, Speech, and Signal ProcessingUniversity of SurreyGuildfordUK

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