Managing multimedia information in database systems
Communications of the ACM
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
Maintaining knowledge about temporal intervals
Communications of the ACM
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 Classification of Tennis Video for High-level Content-based Retrieval
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
Automatic Parsing of TV Soccer Programs
ICMCS '95 Proceedings of the International Conference on Multimedia Computing and Systems
Discovering the hidden structure of complex dynamic systems
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Scalable feature-based video retrieval for mobile devices
Proceedings of the First International Conference on Internet Multimedia Computing and Service
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As amounts of publicly available video data grow, the need to automatically infer semantics from raw video data becomes significant. In this paper, we address the use of three different techniques that support that task, namely, spatio-temporal rule-based method, hidden Markov models, and dynamic Bayesian networks. First, the application of these techniques for detection and recognition of diverse events is briefly described using two case studies (Tennis and Formula 1). We explain the relationships and differences of the three approaches, as well as benefits of their integrated use. Then the focus is moved to the main point of the paper, which is the integration of the aforementioned techniques within a database management system, which provides efficient, flexible, scalable, and domain independent content-based video retrieval. We identify and consider the most important issues when extending a traditional database management system with content-based video retrieval functionality, namely issues concerning video data models, dynamic feature extraction, and extensions of different layers of database architecture. The advantages of the integrated system are demonstrated on examples from our two case studies.