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Multiple Kernel Learning via Distance Metric Learning for Interactive Image Retrieval

Yan, Fei, Mikolajczyk, Krystian and Kittler, Josef (2011) Multiple Kernel Learning via Distance Metric Learning for Interactive Image Retrieval In: International Workshop on Multiple Classifier Systems, 15-17 Jun 2011, Naples, Italy.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
NameEmailORCID
Yan, FeiF.Yan@surrey.ac.ukUNSPECIFIED
Mikolajczyk, KrystianK.Mikolajczyk@surrey.ac.ukUNSPECIFIED
Kittler, JosefJ.Kittler@surrey.ac.ukUNSPECIFIED
Date : 2011
Identification Number : 10.1007/978-3-642-21557-5_17
Copyright Disclaimer : Copyright 2011 Springer-Verlag GmbH Berlin Heidelberg. This is the author's accepted version. The original publication is available at <a href="http://www.springerlink.com">http://www.springerlink.com</a>
Contributors :
ContributionNameEmailORCID
http://www.loc.gov/loc.terms/relators/EDTSansone, CUNSPECIFIEDUNSPECIFIED
http://www.loc.gov/loc.terms/relators/EDTKittler, JUNSPECIFIEDUNSPECIFIED
http://www.loc.gov/loc.terms/relators/EDTRoli, FUNSPECIFIEDUNSPECIFIED
Depositing User : Symplectic Elements
Date Deposited : 21 Dec 2012 12:26
Last Modified : 31 Oct 2017 14:49
URI: http://epubs.surrey.ac.uk/id/eprint/733275

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