Maintaining knowledge about temporal intervals
Communications of the ACM
Video Data Mining: Semantic Indexing and Event Detection from the Association Perspective
IEEE Transactions on Knowledge and Data Engineering
Live sports event detection based on broadcast video and web-casting text
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Mining Nonambiguous Temporal Patterns for Interval-Based Events
IEEE Transactions on Knowledge and Data Engineering
Unsupervised content-based indexing of sports video
Proceedings of the international workshop on Workshop on multimedia information retrieval
Personalized multimedia retrieval: the new trend?
Proceedings of the international workshop on Workshop on multimedia information retrieval
Multimedia event-based video indexing using time intervals
IEEE Transactions on Multimedia
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This paper proposes a new linguistic-perceptual event model tailoring to spatio-temporal event detection and conceptual-visual personalized retrieval of sports video sequences. The major contributions of the proposed model are hierarchical structure, independence between linguistic and perceptual part, and ability of capturing temporal information of sports events. Thanks to these advanced contributions, it is very easy to upgrade model events from simple to complex levels either by self-studying from inner knowledge or by being taught from plug-in additional knowledge. Thus, the proposed model not only can work well in unwell structured environments but also is able to adapt itself to new domains without the need (or with a few modification) for external re-programming, re-configuring and re-adjusting. Thorough experimental results demonstrate that events are modeled and detected with high accuracy and automation, and users' expectation of personalized retrieval is highly satisfied.