X Zhu, WG Aref, J Fan, AC Catlin, AK Elmagarmid

article

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.

PDF

2003 – 03

Data Engineering, 2003. Proceedings. 19th International Conference on

Aref

569- 580

2003-03-01

data mining, database indexing, video databases, video signal processing audio processing technique, database access, database indexing, events mining, frame selection strategy, medical video mining, scene clustering, video content structure, video database management framework, video group merging, video indexing, video processing technique, video shot grouping, video shot segmentation, video skimming tool, video stream