A Structured Visual Learning Approach Mixed with Ontology Dimensions for Medical Queries
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.
KeywordsQuery Image Vector Space Model Visual Concept Query Vector Ontology Dimension
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- 3.Guyot, J., Radhouani, S., Falquet, G.: Ontology-based multilingual information retrieval. In: CLEF Workhop, Working Notes Multilingual Track, Vienna, Austria (2005)Google Scholar
- 4.Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proceedings of International Conference on New Methods in Language Processing (1994)Google Scholar
- 5.Chevallet, J.P.: X-iota: An open xml framework for ir experimentation application on multiple weighting scheme tests in a bilingual corpus. In: AIRS 2004 Conference. LNCS, vol. 3211, pp. 263–280. Springer, Heidelberg (2004)Google Scholar