Different Learning Strategies in a Case-Based Reasoning System for Image Interpretation

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.