Managing multimedia information in database systems
Communications of the ACM
Visual information retrieval
Detecting topical events in digital video
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Automatically extracting highlights for TV Baseball programs
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Motion-Based Recognition
Semantic Modeling and Knowledge Representation in Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
A Survey on Content-Based Retrieval for Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
Flattening an Object Algebra to Provide Performance
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Automatic Parsing of TV Soccer Programs
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A semantic event-detection approach and its application to detecting hunts in wildlife video
IEEE Transactions on Circuits and Systems for Video Technology
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Content-based retrieval has been identified as one of the most challenging problems, requiring a multidisciplinary research among computer vision, information retrieval, artificial intelligence, database, and other fields. In this paper, we address the specific aspect of inferring semantics automatically from raw video data. In particular, we present the Cobra video database management system that supports the integrated use of different knowledge-based methods for mapping low-level features to high-level concepts. We focus on dynamic Bayesian networks and demonstrate how they can be effectively used for fusing the evidence obtained from different media information sources. The approach is validated in the particular domain of Formula 1 race videos. For that specific domain we introduce a robust audio-visual feature extraction scheme and a text recognition and detection method. Based on numerous experiments performed with DBNs, we give some recommendations with respect to the modeling of temporal dependences and different learning algorithms. Finally, we present the experimental results for the detection of excited speech and the extraction of highlights, as well as the advantageous query capabilities of our system.