A structured visual learning approach mixed with ontology dimensions for medical queries

Authors:
Jean-Pierre Chevallet;Joo-Hwee Lim;Saïd Radhouani
Affiliations:
IPAL-CNRS, Institute for Infocomm Research;Institute for Infocomm Research, Singapore;Centre universitaire d’informatique, Genève 4, Switzerland
Venue:
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
Year:
2005

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Abstract

Precise image and text indexing requires domain knowledge and a learning process. In this paper, we present the use of an ontology to filter medical documents and of visual concepts to describe and index associated images. These visual concepts are meaningful medical terms with associated visual appearance from image samples that are manually designed and learned from examples. Text and image indexing processes are performed in parallel and merged to answer mixed-mode queries. We show that fusion of these two methods are of a great benefit and that external knowledge stored in an ontology is mandatory to solve precise queries and provide the overall best results.