Enhancing Recognition of Visual Concepts with Primitive Color Histograms via Non-sparse Multiple Kernel Learning

  • Alexander Binder
  • Motoaki Kawanabe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6242)


In order to achieve good performance in image annotation tasks, it is necessary to combine information from various image features. In recent competitions on photo annotation, many groups employed the bag-of-words (BoW) representations based on the SIFT descriptors over various color channels. In fact, it has been observed that adding other less informative features to the standard BoW degrades recognition performances. In this contribution, we will show that even primitive color histograms can enhance the standard classifiers in the ImageCLEF 2009 photo annotation task, if the feature weights are tuned optimally by non-sparse multiple kernel learning (MKL) proposed by Kloft et al.. Additionally, we will propose a sorting scheme of image subregions to deal with spatial variability within each visual concept.


Color Histogram Equal Error Rate Multiple Kernel Learning Sift Descriptor Average Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexander Binder
    • 1
  • Motoaki Kawanabe
    • 1
    • 2
  1. 1.Fraunhofer Institute FIRSTBerlinGermany
  2. 2.TU BerlinBerlinGermany

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