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Different learning strategies in a case-based reasoning system for image interpretation

  • Petra Perner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1488)

Abstract

In our previous work, we introduced the basic structure of a case-based reasoning system for image interpretation, a structural similarity measure, and some fundamental learning techniques. In this paper, we describe more sophisticated learning techniques that are different in abstraction level. We evaluate our method on a set of images from the non-destructive testing domain and show the feasibility of the approach. As result, we can show that conventional image processing methods combined with machine learning techniques form a powerful tool for image interpretation.

Keywords

CBR Learning Incremental Learning Image Interpretation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Petra Perner
    • 1
  1. 1.Institute of Computer Vision and Applied Computer SciencesLeipzig

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