Abstract
We present the first results of a research aimed at generating useradapted image descriptions from annotated knowledge sources. This system employs a User Model and several knowledge sources to select the image attributes to include in the description and the level of detail. Both ‘individual’ and ‘comparative-descriptions’ may be generated, by taking an appropriate ‘reference’ image according to the context and to an ontology of concepts in the domain to which the image refers; the comparison strategy is suited to the User background and to the interaction history. All data employed in the generation of these descriptions (the image, the discourse) are annotated by a XML-like language. Results obtained in the description of radiological images are presented, and the advantage of annotating knowledge sources are discussed.
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Cassotta, M.T., De Carolis, B., de Rosis, F., Andreoli, C., De Cicco, M.L. (2001). User-Adapted Image Descriptions from Annotated Knowledge Sources. In: Esposito, F. (eds) AI*IA 2001: Advances in Artificial Intelligence. AI*IA 2001. Lecture Notes in Computer Science(), vol 2175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45411-X_29
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DOI: https://doi.org/10.1007/3-540-45411-X_29
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