Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Cross-Language Evaluation Forum: Objectives, Results, Achievements
Information Retrieval
Computer Manual in MATLAB to Accompany Pattern Classification, Second Edition
Computer Manual in MATLAB to Accompany Pattern Classification, Second Edition
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Evaluation axes for medical image retrieval systems: the imageCLEF experience
Proceedings of the 13th annual ACM international conference on Multimedia
Acceleration Strategies for Gaussian Mean-Shift Image Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
The CLEF 2005 Automatic Medical Image Annotation Task
International Journal of Computer Vision
Overview of the ImageCLEFmed 2006 medical retrieval and medical annotation tasks
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Medical image retrieval and automated annotation: OHSU at ImageCLEF 2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Cubic-splines neural network- based system for image retrieval
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In 2006 and 2007, Oregon Health and Science University (OHSU) participated in the automatic image annotation task for medical images at ImageCLEF, an annual international benchmarking event that is part of the cross language evaluation forum (CLEF). The goal of the automatic annotation task was to classify 1000 test images based on the image retrieval in medical applications (IRMA) code, given a set of 10,000 training images. There were 116 distinct classes in 2006 and 2007. We evaluated the efficacy of a variety of primarily global features for this classification task. These included features based on histograms, gray level correlation matrices and the gist technique. A multitude of classifiers including k-nearest neighbors, two-level neural networks, support vector machines, and maximum likelihood classifiers were evaluated. Our official error rates for the 1000 test images were 26% in 2006 using the flat classification structure. The error count in 2007 was 67.8 using the hierarchical classification error computation based on the IRMA code in 2007. Confusion matrices as well as clustering experiments were used to identify visually similar classes. The use of the IRMA code did not help us in the classification task as the semantic hierarchy of the IRMA classes did not correspond well with the hierarchy based on clustering of image features that we used. Our most frequent misclassification errors were along the view axis. Subsequent experiments based on a two-stage classification system decreased our error rate to 19.8% for the 2006 dataset and our error count to 55.4 for the 2007 data.