Ontology matching for the semantic annotation of images

The linguistic description, i.e. semantic annotation of images can benefit from representations of useful concepts and the links between them as ontologies. Recently, several multimedia ontologies have been proposed in the literature as suitable knowledge models to bridge the well known semantic gap between low level features of image content and its high level conceptual meaning. Nevertheless, these multimedia ontologies are often dedicated to (or initially built for) particular needs or a particular application. Ontology matching, defined as the process of relating different heterogeneous models, could be a suitable approach to solve several interoperability issues that coexist in semantic image annotation and retrieval. In this paper, we propose an original and generic instance-based ontology matching approach and a methodology to extract a minimal ontology defined as the common reference between different heterogeneous ontologies. Then, this approach is applied to two different semantic image retrieval issues: the bridging of the semantic gap by the matching of a multimedia ontology with a common-sense knowledge ontology and the matching of different multimedia ontologies to extract a common reference knowledge model dedicated to several multimedia applications.