PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Insights from Classifying Visual Concepts with Multiple Kernel Learning
Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Mueller, Wojciech Samek, Ulf Brefeld, Klaus-Robert Müller and Motoaki Kawanabe
CoRR / Volume abs/1112.3697, pp. 1-18, 2011.


Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, so-called 1-norm MKL variants are often observed to be outperformed by an unweighted sum kernel. The contribution of this paper is twofold: We apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks within computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum kernel SVM and the sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. About to be submitted to PLoS ONE

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:9407
Deposited By:Alexander Binder
Deposited On:16 March 2012