A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
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
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
The CLEF 2005 Automatic Medical Image Annotation Task
International Journal of Computer Vision
Stripe: image feature based on a new grid method and its application in ImageCLEF
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
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
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This paper presents the methods and experimental results for the automatic medical image annotation and retrieval task of ImageCLEFmed 2005. A supervised machine learning approach to associate low-level image features with their high level visual and/or semantic categories is investigated. For automatic image annotation, the input images are presented as a combined feature vector of texture, edge and shape features. A multi-class classifier based on pairwise coupling of several binary support vector machine is trained on these inputs to predict the categories of test images. For visual only retrieval, a combined feature vector of color, texture and edge features is utilized in low dimensional PCA sub-space. Based on the online category prediction of query and database images by the classifier, pre-computed category specific first and second order statistical parameters are utilized in a Bhattacharyya distance measure. Experimental results of both image annotation and retrieval are reported in this paper.