Istituto di Scienza e Tecnologie dell'Informazione     
Little S., Salvetti O., Perner P. Semi-automatic semantic tagging of 3D images from pancreas cells. Petra Perner and Ovidio Salvetti (eds.). (Lecture Notes in Artificial Intelligence, vol. 4826). Heidelberg - Berlin: Springer Verlag, 2007.
Detailed, consistent semantic annotation of large collections of multimedia data is difficult and time-consuming. In domains such as eScience, digital curation and industrial monitoring, fine-grained high-quality labeling of regions enables advanced semantic querying, analysis and aggregation and supports collaborative research. Manual annotation is inefficient and too subjective to be a viable solution. Automatic solutions are often highly domain or application specific, require large volumes of annotated training corpi and, if using a 'black box' approach, add little to the overall scientific knowledge. This article evaluates the use of simple artificial neural networks to semantically annotate micrographs and discusses the generic process chain necessary for semi-automatic semantic annotation of images.
URL: http://www.springerlink.com/content/b4024796wq1625q7/fulltext.pdf
Subject Multimedia semantic annotation
Semantic gap
Artificial neural networks
I.2.4 Knowledge Representation Formalisms and Methods
I.2.10 Vision and Scene Understanding
I.4.9 Applications

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