Multi-task Learning via Non-sparse Multiple Kernel Learning

  • Wojciech Samek
  • Alexander Binder
  • Motoaki Kawanabe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6854)


In object classification tasks from digital photographs, multiple categories are considered for annotation. Some of these visual concepts may have semantic relations and can appear simultaneously in images. Although taxonomical relations and co-occurrence structures between object categories have been studied, it is not easy to use such information to enhance performance of object classification. In this paper, we propose a novel multi-task learning procedure which extracts useful information from the classifiers for the other categories. Our approach is based on non-sparse multiple kernel learning (MKL) which has been successfully applied to adaptive feature selection for image classification. Experimental results on PASCAL VOC 2009 data show the potential of our method.


Image Annotation Multi-Task Learning Multiple Kernel Learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Wojciech Samek
    • 1
    • 2
  • Alexander Binder
    • 1
  • Motoaki Kawanabe
    • 2
    • 1
  1. 1.Technical University of BerlinBerlinGermany
  2. 2.Fraunhofer Institute FIRSTBerlinGermany

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