Abstract
To achieve more efficient video indexing and access, we introduce a video database management framework and strategies for video content structure and events mining. The video shot segmentation and representative frame selection strategy are first utilized to parse the continuous video stream into physical units. Video shot grouping, group merging, and scene clustering schemes are then proposed to organize the video shots into a hierarchical structure using clustered scenes, scenes, groups, and shots, in increasing granularity from top to bottom. Then, audio and video processing techniques are integrated to mine event information, such as dialog, presentation and clinical operation, from the detected scenes. Finally, the acquired video content structure and events are integrated to construct a scalable video skimming tool which can be used to visualize the video content hierarchy and event information for efficient access. Experimental results are also presented to evaluate the performance of the proposed framework and algorithms.
Original language | English |
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Pages | 569-580 |
Number of pages | 12 |
DOIs | |
Publication status | Published - 2003 |
Externally published | Yes |
Event | Nineteenth International Conference on Data Ingineering - Bangalore, India Duration: 5 Mar 2003 → 8 Mar 2003 |
Conference
Conference | Nineteenth International Conference on Data Ingineering |
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Country/Territory | India |
City | Bangalore |
Period | 5/03/03 → 8/03/03 |