Ontology-Based Automatic Image Annotation Exploiting Generalized Qualitative Spatial Semantics
Ontologies provide a formal approach to knowledge representation suitable for digital content annotation. In the context of image annotation a variety of ontology-based tools has been proposed. Most of them enable manual annotation of the images with higher level concepts whereas many of them are capable of formally representing low-level features as well. However, they either consider specific, usually quantitative, representations of the low-level features, or spatial semantics limited to 2D/3D image spaces. In this paper we propose a novel ontology-based methodology for automatic image annotation that exploits generalized qualitative spatial relations between objects, given an image domain. To represent knowledge for the spatial arrangements, we have implemented an ontology that models spatial relations in multi-dimensional vector spaces. The application of the proposed methodology is demonstrated for automatic annotation of segmented objects in chest radiographs.
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