Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Content-Based Image Retrieval at the End of the Early Years
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
A Unifying View of Image Similarity
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
IEEE Transactions on Software Engineering
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
FIRE – flexible image retrieval engine: ImageCLEF 2004 evaluation
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
Efficiently support concurrent queries in multiuser CBIR systems
Multimedia Tools and Applications
Combining visual features for medical image retrieval and annotation
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
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We propose a novel framework for content-based image retrieval with multiple parallel retrieval engines (MultiPRE) to achieve higher retrieval performance. Visual features, including both low-level features, such as color, texture and region features, and middle-level structure features, such as blob representation of objects are used to capture geometrical and statistical characteristics of images. Both clustering analysis and discrimination analysis are used as similarity measures in multiple retrieval engines, which are based on~principal component analysis (PCA) and support vector machines (SVM), respectively. Finally outputs of these engines are fused to determine ranking lists of retrieved images for given retrieval topics. The proposed framework has been evaluated based on the 26 image query topics over the CasImage database~with over 9000 medical images~used in ImageCLEF 2004, an international research effort for content-based image retrieval performance benchmark. Experiments show that the proposed framework achieved significantly better performance in terms of both the mean and the variance of average precision than the best run reported in ImageCLEF2004.