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11 October 2000 Knowledge-based inference engine for online video dissemination
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Proceedings Volume 4210, Internet Multimedia Management Systems; (2000) https://doi.org/10.1117/12.403818
Event: Information Technologies 2000, 2000, Boston, MA, United States
Abstract
To facilitate easy access to rich information of multimedia over the Internet, we develop a knowledge-based classification system that supports automatic Indexing and filtering based on semantic concepts for the dissemination of on-line real-time media. Automatic segmentation, annotation and summarization of media for fast information browsing and updating are achieved in the same time. In the proposed system, a real-time scene-change detection proxy performs an initial video structuring process by splitting a video clip into scenes. Motional and visual features are extracted in real time for every detected scene by using online feature extraction proxies. Higher semantics are then derived through a joint use of low-level features along with inference rules in the knowledge base. Inference rules are derived through a supervised learning process based on representative samples. On-line media filtering based on semantic concepts becomes possible by using the proposed video inference engine. Video streams are either blocked or sent to certain channels depending on whether or not the video stream is matched with the user's profile. The proposed system is extensively evaluated by applying the engine to video of basketball games.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Wensheng Zhou and C.-C. Jay Kuo "Knowledge-based inference engine for online video dissemination", Proc. SPIE 4210, Internet Multimedia Management Systems, (11 October 2000); https://doi.org/10.1117/12.403818
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