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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Lanckriet, G., Cristianini, N., Bartlett, P., Ghaoui, L.E., Jordan, M.: Learning the kernel matrix with semidefinite programming. JMLR 5, 27–72 (2004)
Bach, F., Lanckriet, G.: Multiple kernel learning, conic duality, and the smo algorithm. In: ICML (2004)
Sonnenburg, S., Ratsch, G., Schafer, C., Scholkopf, B.: Large scale multiple kernel learning. JMLR 7, 1531–1565 (2006)
Xing, E., Ng, A., Jordan, M., Russell, S.: Distance metric learning, with application to clustering with side-information. In: NIPS (2002)
Schultz, M., Joachims, T.: Learning a distance metric from relative comparisons. In: NIPS (2004)
Weinberger, K., Saul, L.: Distance metric learning for large margin nearest neighbor classification. JMLR 10, 207–244 (2009)
Kloft, M., Brefeld, U., Sonnenburg, S., Zien, A.: Efficient and accurate. In: NIPS (2009)
Ye, J., Ji, S., Chen, J.: Multi-class discriminant kernel learning via convex programming. JMLR 9, 719–758 (2008)
Yan, F., Mikolajczyk, K., Barnard, M., Cai, H., Kittler, J.: Lp norm multiple kernel fisher discriminant analysis for object and image categorisation. In: CVPR (2010)
Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Lin, S.: Graph embedding and extensions: A general framework for dimensionality reduction. In: PAMI (2007)
Nilsback, M., Zisserman, A.: Automated flower classification over a large number of classes. In: Indian Conference on Computer Vision, Graphics and Image Processing (2008)
Fei-Fei, L., Fergus, R., Perona, P.: One-shot learning of object categories. PAMI 28(4), 594–611 (2006)
Sande, K., Gevers, T., Snoek, C.: Evaluation of color descriptors for object and scene recognition. In: CVPR (2008)
Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: ICCV (2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yan, F., Mikolajczyk, K., Kittler, J. (2011). Multiple Kernel Learning via Distance Metric Learning for Interactive Image Retrieval. In: Sansone, C., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2011. Lecture Notes in Computer Science, vol 6713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21557-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-21557-5_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21556-8
Online ISBN: 978-3-642-21557-5
eBook Packages: Computer ScienceComputer Science (R0)