Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Refining video annotation by exploiting pairwise concurrent relation
Proceedings of the 15th international conference on Multimedia
Video Event Recognition Using Kernel Methods with Multilevel Temporal Alignment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimedia ontology learning for automatic annotation and video browsing
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Nrityakosha: Preserving the intangible heritage of Indian classical dance
Journal on Computing and Cultural Heritage (JOCCH)
Modeling BharataNatyam dance steps: art to SMart
Proceedings of the CUBE International Information Technology Conference
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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.