An Ontology Framework For Knowledge-Assisted Semantic Video Analysis and Annotation

Abstract. An approach for knowledge assisted semantic analysis and annotation of video content, based on an ontology infrastructure is presented. Semantic concepts in the context of the examined domain are defined in an ontology, enriched with qualitative attributes of the semantic objects (e.g. color homogeneity), multimedia processing methods (color clustering, respectively), and numerical data or low-level features generated via training (e.g. color models, also defined in the ontology). Semantic Web technologies are used for knowledge representation in RDF/RDFS language. Rules in F-logic are defined to describe how tools for multimedia analysis should be applied according to different object attributes and low-level features, aiming at the detection of video objects corresponding to the semantic concepts defined in the ontology. This supports flexible and managed execution of various application and domain independent multimedia analysis tasks. This ontology-based approach provides the means of generating semantic metadata and as a consequence Semantic Web services and applications have a greater chance of discovering and exploiting the information and knowledge in multimedia data. The proposed approach is demonstrated in the Formula One and Football domains and shows promising results.