Feature Selection for Automatic Image Annotation

  • Lokesh Setia
  • Hans Burkhardt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


Automatic image annotation empowers the user to search an image database using keywords, which is often a more practical option than a query-by-example approach. In this work, we present a novel image annotation scheme which is fast and effective and scales well to a large number of keywords. We first provide a feature weighting scheme suitable for image annotation, and then an annotation model based on the one-class support vector machine. We show that the system works well even with a small number of visual features. We perform experiments using the Corel Image Collection and compare the results with a well-established image annotation system.


Feature Selection Discrete Wavelet Transformation Query Image Image Annotation Positive Class 
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 2006

Authors and Affiliations

  • Lokesh Setia
    • 1
  • Hans Burkhardt
    • 1
  1. 1.Albert-Ludwigs-University FreiburgFreiburg im BreisgauGermany

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