Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experimental evaluation of expert fusion strategies
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 13th annual ACM international conference on Multimedia
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
The CLEF 2004 cross-language image retrieval track
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
The CLEF 2005 Automatic Medical Image Annotation Task
International Journal of Computer Vision
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In this paper we report our work using visual feature fusion for the tasks of medical image retrieval and annotation in the benchmark of ImageCLEF 2005. In the retrieval task, we use visual features without text information, having no relevance feedback. Both local and global features in terms of both structural and statistical nature are captured. We first identify visually similar images manually and form templates for each query topic. A pre-filtering process is utilized for a coarse retrieval. In the fine retrieval, two similarity measuring channels with different visual features are used in parallel and then combined in the decision level to produce a final score for image ranking. Our approach is evaluated over all 25 query topics with each containing example image(s) and topic textual statements. Over 50,000 images we achieved a mean average precision of 14.6%, as one of the best performed runs. In the annotation task, visual features are fused in an early stage by concatenation with normalization. We use support vector machines (SVM) with RBF kernels for the classification. Our approach is trained over a 9,000 image training set and tested over the given test set with 1000 images and on 57 classes with a correct classification rate of about 80%.