Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Evaluation of Dissimilarity Measures for Color and Texture
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Prototype selection for dissimilarity-based classifiers
Pattern Recognition
The CLEF 2005 cross–language image retrieval track
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
A framework and baseline results for the CLEF medical automatic annotation task
Pattern Recognition Letters
Image Annotation Using Sub-block Energy of Color Correlograms
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Content based radiology image retrieval using a fuzzy rule based scalable composite descriptor
Multimedia Tools and Applications
Baseline results for the ImageCLEF 2006 medical automatic annotation task
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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A combination of several classifiers using global features for the content description of medical images is proposed. Beside well known texture histogram features, downscaled representations of the original images are used, which preserve spatial information and utilize distance measures which are robust with regard to common variations in radiation dose, translation, and local deformation. These features were evaluated for the annotation task and the retrieval task in ImageCLEF 2005 without using additional textual information or query refinement mechanisms. For the annotation task, a categorization rate of 86.7% was obtained, which ranks second among all submissions. When applied in the retrieval task, the image content descriptors yielded a mean average precision (MAP) of 0.0751, which is rank 14 of 28 submitted runs. As the image deformation model is not fit for interactive retrieval tasks, two mechanisms are evaluated with regard to the trade-off between loss of accuracy and speed increase: hierarchical filtering and prototype selection.