An approach to the automatic construction of global thesauri
Information Processing and Management: an International Journal
A fuzzy document retrieval system using the keyword connection matrix and a learning method
Fuzzy Sets and Systems - Special issue on applications of fuzzy systems theory, Iizuka '88
The nature of statistical learning theory
The nature of statistical learning theory
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Local Feedback in Full-Text Retrieval Systems
Journal of the ACM (JACM)
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Modern Information Retrieval
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Overview of the ImageCLEFmed 2008 medical image retrieval task
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
MCBR-CDS'09 Proceedings of the First MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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A unified medical image retrieval framework integrating visual and text keywords using a novel multi-modal query expansion (QE) is presented. For the content-based image search, visual keywords are modeled using support vector machine (SVM)-based classification of local color and texture patches from image regions. For the text-based search, keywords from the associated annotations are extracted and indexed. The correlations between the keywords in both the visual and text feature spaces are analyzed for QE by considering local feedback information. The QE approach can propagate user perceived semantics from one modality to another and improve retrieval effectiveness when combined in multi-modal search. An evaluation of the method on imageCLEFmed'08 dataset and topics results in a mean average precision (MAP) score of 0.15 over comparable searches without QE or using only single modality.