Insights from classifying visual concepts with multiple kernel learning

Alexander Binder, Shinichi Nakajima, Marius Kloft, Christina Müller, Wojciech Samek, Ulf Brefeld, Klaus Robert Müller, Motoaki Kawanabe

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.tuberlin.de/image_mkl/(Accessed 2012 Jun 25).

Original languageEnglish
Article numbere38897
JournalPloS one
Volume7
Issue number8
DOIs
Publication statusPublished - 2012 Aug 24

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ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Cite this

Binder, A., Nakajima, S., Kloft, M., Müller, C., Samek, W., Brefeld, U., Müller, K. R., & Kawanabe, M. (2012). Insights from classifying visual concepts with multiple kernel learning. PloS one, 7(8), [e38897]. https://doi.org/10.1371/journal.pone.0038897