{"title":"A Comparison of L_1 Norm and L_2 Norm Multiple Kernel SVMs in Image and Video Classification","authors":"F. Yan, K. Mikolajczyk, J. Kittler, M. Tahir","doi":"10.1109/CBMI.2009.44","DOIUrl":null,"url":null,"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.","PeriodicalId":417012,"journal":{"name":"2009 Seventh International Workshop on Content-Based Multimedia Indexing","volume":"136 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Seventh International Workshop on Content-Based Multimedia Indexing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBMI.2009.44","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
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.
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