PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Little S., Salvetti O., Perner P. Semi-automatic semantic annotation of images. In: Workshop-Woche: Lernen-Wissen-Adaption. LWA 2007 (Halle - Germany, 24-26 September 2007). Proceedings, pp. 113 - 118. Alexander Hinneburg (ed.). Martin-Luther-University Halle-Wittenberg, 2007.
 
 
Abstract
(English)
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, finegrained 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.
Subject Neural Networks
Semantic Annotation
I.2.10 Vision and Scene Understanding
J.3 Life and Medical Sciences


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