An SVM Confidence-Based Approach to Medical Image Annotation

  • Tatiana Tommasi
  • Francesco Orabona
  • Barbara Caputo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)


This paper presents the algorithms and results of the “idiap” team participation to the ImageCLEFmed annotation task in 2008. On the basis of our successful experience in 2007 we decided to integrate two different local structural and textural descriptors. Cues are combined through concatenation of feature vectors and through the Multi-Cue Kernel. The challenge this year was to annotate images coming mainly from classes with only few training examples. We tackled the problem on two fronts: (1) we introduced a further integration strategy using SVM as an opinion maker; (2) we enriched the poorly populated classes adding virtual examples. We submitted several runs considering different combinations of the proposed techniques. The run jointly using the feature concatenation, the confidence-based opinion fusion and the virtual examples ranked first among all submissions.


Feature Vector Image Retrieval Local Binary Pattern Scale Invariant Feature Transform Error Score 
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 2009

Authors and Affiliations

  • Tatiana Tommasi
    • 1
  • Francesco Orabona
    • 1
  • Barbara Caputo
    • 1
  1. 1.Idiap Research InstituteMartignySwitzerland

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