Content-based Image Retrieval by Ontology-based Object Recognition

The main disadvantage of image retrieval systems is their lack of domain knowledge. Therefore a retrieval system has to focus on primitive features, as Eakins and Graham name them [3]. Due to the lack of background knowledge of the domain, the retrieval error rate is usually dissatisfying or the search options are limited to syntactic queries. Knowledgebased techniques allow for semantical searches filling the “semantical gap” [4]. In this paper we present a supervised learning system OntoPic, which is based on the well-known ontologies coded in DAML+OIL, for providing the domain knowledge. Combined with a DL reasoner for ontologies, the main target is to achieve a new level of result quality while allowing semantical searches. The main advantage of this approach is the usage of the reasoner as a classifier, enabling a dual use of the ontology. The same domain knowledge can be used for better object recognition, the basis for satisfying results, and a semantical search. Our work is applied to the domain of landscape images.