Using Concept Recognition to Annotate a Video Collection
In this paper, we propose a scheme based on an ontological framework, to recognize concepts in multimedia data, in order to provide effective content-based access to a closed, domain-specific multimedia collection. The ontology for the domain is constructed from high-level knowledge of the domain lying with the domain experts, and further fine-tuned and refined by learning from multimedia data annotated by them. MOWL, a multimedia extension to OWL, is used to encode the concept to media-feature associations in the ontology as well as the uncertainties linked with observation of the perceptual multimedia data. Media feature classifiers help recognize low-level concepts in the videos, but the novelty of our work lies in discovery of high-level concepts in video content using the power of ontological relations between the concepts. This framework is used to provide rich, conceptual annotations to the video database, which can further be used to create hyperlinks in the video collection, to provide an effective video browsing interface to the user.
KeywordsBayesian Network Multimedia Data Video Annotation Concept Recognition Ontology Learning
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