C4.5: programs for machine learning
C4.5: programs for machine learning
Motion recovery for video content classification
ACM Transactions on Information Systems (TOIS) - Special issue on video information retrieval
A society of models for video and image libraries
IBM Systems Journal
Knowledge-based image understanding systems: a survey
Computer Vision and Image Understanding
VideoQ: an automated content based video search system using visual cues
MULTIMEDIA '97 Proceedings of the fifth ACM international conference on Multimedia
Semantic multicast: intelligently sharing collaborative sessions
ACM Computing Surveys (CSUR)
Machine Learning
Content-Based Classification, Search, and Retrieval of Audio
IEEE MultiMedia
Automatic Classification of Tennis Video for High-level Content-based Retrieval
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
ImageRover: A Content-Based Image Browser for the World Wide Web
CAIVL '97 Proceedings of the 1997 Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97)
Color image quantization for frame buffer display
SIGGRAPH '82 Proceedings of the 9th annual conference on Computer graphics and interactive techniques
Rapid scene analysis on compressed video
IEEE Transactions on Circuits and Systems for Video Technology
Sports video summarization using highlights and play-breaks
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Video Semantic Content Analysis Framework Based on Ontology Combined MPEG-7
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Movie story intensity representation through audiovisual tempo analysis
Multimedia Tools and Applications
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Current information and communication technologies provide the infrastructure to transport bits anywhere, but do not indicate how to easily and precisely access and/or route information at the semantic level. To facilitate intelligent access to the rich multimedia data over the Internet, we develop an on-line knowledge- and rule-based video classification system that supports automatic "indexing" and "filtering" based on the semantic concept hierarchy. This paper investigates the use of video and audio content analysis, feature extraction and clustering techniques for further video semantic concept classification. A supervised rule-based video classification system is proposed using video automatic segmentation, annotation and summarization techniques for seamless information browsing and updating. 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, visual and audio features are extracted in real-time for every detected scene by using on-line feature-extraction proxies. Higher semantics are then derived through a joint use of low-level features along with classification rules in the knowledge base. Classification rules are derived through a supervised learning process that relies on some representative samples from each semantic category. An indexing and filtering process can now be built using the semantic concept hierarchy to personalize multimedia data based on users' interests. In real-time filtering, multiple video streams are blocked, combined, or sent to certain channels depending on whether or not the video streams are matched with the user's profile. We have extensively experimented and evaluated the classification and filtering techniques using basketball sports video data. In particular, in our experiment, the basketball video structure is examined and categorized into different classes according to distinct motional, visual and audio characteristics features by a rule-based classifier. The concept hierarchy describing the motional/visual/audio feature descriptors and their statistical relationships are reported in this paper along with detailed experimental results using on-line sports videos.