Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Using human perceptual categories for content-based retrieval from a medical image database
Computer Vision and Image Understanding
Discovering recurrent image semantics from class discrimination
EURASIP Journal on Applied Signal Processing
VisMed: a visual vocabulary approach for medical image indexing and retrieval
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
A semantic fusion approach between medical images and reports using UMLS
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
X-IOTA: an open XML framework for IR experimentation
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
Artificial Intelligence in Medicine
Use of language model, phrases and Wikipedia forward links for INEX 2009
INEX'09 Proceedings of the Focused retrieval and evaluation, and 8th international conference on Initiative for the evaluation of XML retrieval
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We promote the use of explicit medical knowledge to solve retrieval of information both visual and textual. For text, this knowledge is a set of concepts from a Meta-thesaurus, the Unified Medical Language System (UMLS). For images, this knowledge is a set of semantic features that are learned from examples using SVM within a structured learning framework. Image and text index are represented in the same way: a vector of concepts. The use of concepts allows the expression of a common index form: an inter-media index, offering the opportunity of homogeneous indexing/querying time fusion techniques. Top results obtained with concept based approaches show the potential of conceptual indexing.