Learning Rules for Semantic Video Event Annotation
VISUAL '08 Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management
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MS '08 Proceedings of the 2nd ACM workshop on Multimedia semantics
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Robust semantic concept detection in large video collections
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Multimedia Tools and Applications
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ACM Transactions on Intelligent Systems and Technology (TIST)
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Multimedia Tools and Applications
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Journal on Image and Video Processing - Special issue on advanced video-based surveillance
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J-MRE '11 Proceedings of the 2011 joint ACM workshop on Modeling and representing events
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Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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International Journal of Business Intelligence and Data Mining
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Proceedings of the 20th ACM international conference on Multimedia
Multimedia Databases and Data Management: A Survey
International Journal of Multimedia Data Engineering & Management
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International Journal of Multimedia Data Engineering & Management
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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
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Artificial Intelligence Review
E-LAMP: integration of innovative ideas for multimedia event detection
Machine Vision and Applications
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In this paper, a subspace-based multimedia data mining framework is proposed for video semantic analysis, specifically video event/concept detection, by addressing two basic issues, i.e., semantic gap and rare event/concept detection. The proposed framework achieves full automation via multimodal content analysis and intelligent integration of distance-based and rule-based data mining techniques. The content analysis process facilitates the comprehensive video analysis by extracting low-level and middle-level features from audio/visual channels. The integrated data mining techniques effectively address these two basic issues by alleviating the class imbalance issue along the process and by reconstructing and refining the feature dimension automatically. The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework. Furthermore, its unique domain-free characteristic indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.