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This paper proposes a new methodology to automatically build semantichierarchies suitable for image annotation and classification. The building ofthe hierarchy is based on a new measure of semantic similarity. The proposedmeasure incorporates several sources of information: visual, conceptual andcontextual as we defined in this paper. The aim is to provide a measure thatbest represents image semantics. We then propose rules based on this measure,for the building of the final hierarchy, and which explicitly encodehierarchical relationships between different concepts. Therefore, the builthierarchy is used in a semantic hierarchical classification framework for imageannotation. Our experiments and results show that the hierarchy built improvesclassification results.  Ce papier propose une nouvelle methode pour la construction automatique dehierarchies semantiques adaptees a la classification et a l'annotationd'images. La construction de la hierarchie est basee sur une nouvelle mesure desimilarite semantique qui integre plusieurs sources d'informations: visuelle,conceptuelle et contextuelle que nous definissons dans ce papier. L'objectifest de fournir une mesure qui est plus proche de la semantique des images. Nousproposons ensuite des regles, basees sur cette mesure, pour la construction dela hierarchie finale qui encode explicitement les relations hierarchiques entreles differents concepts. La hierarchie construite est ensuite utilisee dans uncadre de classification semantique hierarchique d'images en concepts visuels.Nos experiences et resultats montrent que la hierarchie construite permetd'ameliorer les resultats de la classification.

Combinaison d'information visuelle, conceptuelle, et contextuelle pour  la construction automatique de hierarchies semantiques adaptees a  l'annotation d'images