Abstract
This paper provides baseline results for the medical automatic annotation task of CLEF 2007 by applying the image retrieval in medical applications (IRMA)-based algorithms previously used in 2005 and 2006, with identical parameterization. Three classifiers based on global image features are combined within a nearest neighbor (NN) approach: texture histograms and two distance measures, which are applied on down-scaled versions of the original images and model common variabilities in the image data. According to the evaluation scheme introduced in 2007, which uses the hierarchical structure of the coding scheme for the categorization, the baseline classifier yields scores of 51.29 and 52.54 when reporting full codes for 1-NN and 5-NN, respectively. This corresponds to error rates of 20.0% and 18.0% (rank 18 among 68 runs), respectively. Improvements via addressing the code hierarchy were not obtained. However, comparing the baseline results yields that the 2007 task was slightly easier than the previous ones.
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Güld, M.O., Deserno, T.M. (2008). Baseline Results for the ImageCLEF 2007 Medical Automatic Annotation Task Using Global Image Features . In: Peters, C., et al. Advances in Multilingual and Multimodal Information Retrieval. CLEF 2007. Lecture Notes in Computer Science, vol 5152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85760-0_81
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DOI: https://doi.org/10.1007/978-3-540-85760-0_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85759-4
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