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Proceedings Paper

Knowledge-based inference engine for online video dissemination
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Paper 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.

Paper Details

Date Published: 11 October 2000
PDF: 12 pages
Proc. SPIE 4210, Internet Multimedia Management Systems, (11 October 2000); doi: 10.1117/12.403818
Show Author Affiliations
Wensheng Zhou, HRL Labs. (USA) and Univ. of Southern California (United States)
C.-C. Jay Kuo, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 4210:
Internet Multimedia Management Systems
John R. Smith; Chinh Le; Sethuraman Panchanathan; C.-C. Jay Kuo, Editor(s)

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