PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Comparison of L1 Norm and L2 Norm Multiple Kernel SVMs in Image and Video Classification
Fei Yan, Krystian Mikolajczyk, Josef Kittler and Muhammad Tahir
In: International Workshop on Content Based Multimedia Indexing 2009, 3-5 June, 2009, Chania, Greece.

Abstract

SVM is one of the state-of-the-art techniques for image and video classification. When multiple kernels are available, the recently introduced multiple kernel SVM (MK-SVM) learns an optimal linear combination of the kernels, providing a new method for information fusion. In this paper we study how the behaviour of MK-SVM is affected by the norm used to regularise the kernel weights to be learnt. Through experiments on three image/video classification datasets as well as on synthesised data, new insights are gained as to how the choice of regularisation norm should be made, especially when MK-SVM is applied to image/video classification problems.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Multimodal Integration
ID Code:6221
Deposited By:Fei Yan
Deposited On:08 March 2010