Baseline Results for the ImageCLEF 2006 Medical Automatic Annotation Task

The ImageCLEF 2006 medical automatic annotation task encompasses 11,000 images from 116 categories, compared to 57 categories for 10,000 images of the similar task in 2005. As a baseline for comparison, a run using the same classifiers with the identical parameterization as in 2005 is submitted. In addition, the parameterization of the classifier was optimized according to the 9,000/1,000 split of the 2006 training data. In particular, texture-based classifiers are parallel combined with classifiers, which use spatial intensity information to model common variabilities among medical images. However, all individual classifiers are based on global features, i.e. one feature vector describes the entire image. The parameterization from 2005 yields an error rate of

• Type: Conference
• Year: 2006
• Where: CLEF
• Authors: Mark Oliver Güld, Christian Thies, Benedikt Fischer, Thomas Martin Deserno
• Keywords: Automatic Annotation Task, CLEF 2006, Identical Parameterization, Information Management, Texture-based Classifiers