Sports event detection using temporal patterns mining and web-casting text
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
A new spatio-temporal method for event detection and personalized retrieval of sports video
Multimedia Tools and Applications
Multimedia data mining: state of the art and challenges
Multimedia Tools and Applications
Expert Systems with Applications: An International Journal
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
Journal of Visual Communication and Image Representation
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With the proliferation of multimedia data and evergrowing requests for multimedia applications, new challenges are emerged for efficient and effective managing and accessing large audio-visual collections. In this paper, we present a novel framework for video event detection, which plays an essential role in high-level video indexing and retrieval. Especially, since temporal information in a video sequence is critical in conveying video content, a hierarchical temporal association mining approach is developed to systematically capture the characteristic temporal patterns with respect to the events of interest. In this process, the unique challenges caused by the loose video structure and skewed data distribution issues are effectively tackled. In addition, an adaptive mechanism is proposed to determine the essential thresholds which are generally defined manually in the traditional association rule mining (ARM) approach. This framework thus largely relaxes the dependence on the domain knowledge and contributes to the ultimate goal of automatic video content analysis.