Abstract
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, 1-norm regularized MKL variants are often observed to be outperformed by an unweighted sum kernel. The main contributions of this paper are the following: we apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks from the application domain of 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 sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. Data sets (kernel matrices) as well as further information are available at http://doc.ml.tu-berlin.de/image_mkl/(Accessed 2012 Jun 25).
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Algorithms*
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Models, Theoretical
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Pattern Recognition, Automated
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Software*
Grants and funding
This work was supported in part by the Federal Ministry of Economics and Technology of Germany (BMWi) under the project THESEUS (FKZ 01MQ07018), by Federal Ministry of Education and Research of Germany (BMBF) under the project REMIND (FKZ 01-IS07007A), by the German National Science Foundation (DFG) under references GA 1615/1-1, MU 987/6-1 and RA 1894/1-1 and by the FP7-ICT program of the European Community, under the PASCAL2 Network of Excellence (ICT-216886). Marius Kloft acknowledges a scholarship by the German Academic Exchange Service (DAAD). This work was also supported by the World Class University Program through the National Research Foundation of Korea funded by the Korean Ministry of Education, Science, and Technology, under Grant R31-10008. Part of this work was done while KRM was at the Institute of Pure and Applied Mathematics, University of California Los Angeles, USA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.