Using Ontologies to Reduce the Semantic Gap between Historians and Image Processing Algorithms

To reduce the gap between pixel data and thesaurus semantics, this paper presents a novel approach using mapping between two ontologies on images of drop-capitals (also named drop caps or lettrines): In the first ontology, each drop cap image is endowed with semantic information describing its content. It is generated from a database of lettrines images - namely Ornamental Letter Images Data Base - manually populated by historians with drop cap images annotations. For the second ontology we have developed image processing algorithms to extract image regions on the basis of a number of features. These features, as well as spatial relations, among regions form the basis of the ontology. The ontologies are then enriched by inference rules to annotate some regions to automatically deduce their semantics. In this article, the method is presented together with preliminary experimental results and an illustrative example.