Abstract
Image annotation and classification are important areas where pattern recognition algorithms can be applied. In this article we report the insights that we have gained during our participation in the ImageCLEF medical annotation task during the years 2006 and 2007. Grayscale radiograph images taken from clinical routine had to be classified into one of the 116 base classes or labeled with attributes which described various properties of the radiograph. We present an algorithm based on local relational features which is robust with respect to illumination changes. It incorporates the geometric constellation of the feature points during the matching process and thus obtains superior performance. Furthermore, a hierarchical classification scheme is presented which reduces the computational complexity of the classifier.
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