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University of Freiburg at ImageCLEF06 -Radiograph Annotation Using Local Relational
 

Summary: University of Freiburg at ImageCLEF06 -
Radiograph Annotation Using Local Relational
Features
Lokesh Setia, Alexandra Teynor, Alaa Halawani and Hans Burkhardt
Chair of Pattern Recognition and Image Processing (LMB),
Albert-Ludwigs-University Freiburg, Germany
{setia, teynor, halawani, burkhardt} @informatik.uni-freiburg.de
Abstract
This paper provides details of the experiments performed by the LMB group at the
University of Freiburg, Germany, for the medical automatic annotation task in the
ImageCLEF 2006. We use local features calculated around interest points, which
have recently recieved excellent results for various image recognition and classification
tasks. We propose the use of relational features, which are highly robust to illumination
changes, and thus quite suitable for X-Ray images. Results with various feature and
classifier settings are reported. A significant improvement in results is seen when the
relative positions of the interest points are also taken into account during matching.
For the given test set, our best run had a classification error rate of 16.7 %, just
0.5 % higher than the best overall submission, and therewith was ranked second in the
medical automatic annotation task at the ImageCLEF 2006.
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Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung

 

Collections: Computer Technologies and Information Sciences