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.
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© 1998 Springer-Verlag Berlin Heidelberg
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Perner, P. (1998). Different learning strategies in a case-based reasoning system for image interpretation. In: Smyth, B., Cunningham, P. (eds) Advances in Case-Based Reasoning. EWCBR 1998. Lecture Notes in Computer Science, vol 1488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056338
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DOI: https://doi.org/10.1007/BFb0056338
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