A magnifier tool for video data
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Content-Based Video Indexing and Retrieval
IEEE MultiMedia
Content-based browsing of video sequences
MULTIMEDIA '94 Proceedings of the second ACM international conference on Multimedia
Retrieving and visualizing video
Communications of the ACM
Transition network grammars for natural language analysis
Communications of the ACM
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
A Spatio-Temporal Semantic Model for Multimedia Database Systems and Multimedia Information Systems
IEEE Transactions on Knowledge and Data Engineering
OVID: Design and Implementation of a Video-Object Database System
IEEE Transactions on Knowledge and Data Engineering
An Object-Oriented Conceptual Modeling of Video Data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Efficient matching and clustering of video shots
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
Bayesian estimation for multiscale image segmentation
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
A Multimedia Data Mining Framework: Mining Information from Traffic Video Sequences
Journal of Intelligent Information Systems
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In an interactive multimedia information system, users should have the flexibility to browse and choose various scenarios they want to see. This means that two-way communications should be captured by the conceptual model. Digital video has gained increasing popularity in many multimedia applications. Instead of sequential access to the video contents, the structuring and modeling of video data so that users can quickly and easily browse and retrieve interesting materials becomes an important issue in designing multimedia information systems. An abstract semantic model called the augmented transition network (ATN), which can model video data and user interactions, is proposed in this paper. An ATN and its sub-networks can model video data based on different granularities such as scenes, shots and key frames. Multimedia input strings are used as inputs for ATNs. The details of how to use multimedia input strings to model video data are also discussed. Key frame selection is based on temporal and spatial relations of semantic objects in each shot. The temporal and spatial relations of semantic objects are captured from our proposed unsupervised video segmentation method which considers the problem of partitioning each frame as a joint estimation of the partition and class parameter variables. Unlike existing semantic models which only model multimedia presentation, multimedia database searching, or browsing, ATNs together with multimedia input strings can model these three in one framework.