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1 UMR 228 Espace-Dev, Espace pour le développement 2 LIPN - Laboratoire d-Informatique de Paris-Nord 3 QCRI - Qatar Computing Research Institute Doha, Qatar

Abstract : Recent studies have shown that the use of a priori knowledge can significantly improve the results of unsupervised classification. However, capturing and formatting such knowledge as constraints is not only very expensive requiring the sustained involvement of an expert but it is also very difficult because some valuable information can be lost when it cannot be encoded as constraints. In this paper, we propose a new constraint-based clustering approach based on ontology reasoning for automatically generating constraints and bridging the semantic gap in satellite image labeling. The use of ontology as a priori knowledge has many advantages that we leverage in the context of satellite image interpretation. The experiments we conduct have shown that our proposed approach can deal with incomplete knowledge while completely exploiting the available one.

Keywords : Ontlogies Clustering Semi supervised learning Satellite images analysis Description logic reasoning A priori knowledge





Autor: Hatim Chahdi - Nistor Grozavu - Isabelle Mougenot - Laure Berti-Equille - Younès Bennani -

Fuente: https://hal.archives-ouvertes.fr/



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