期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2011
卷号：2
期号：6
页码：2710-2713
出版社：TechScience Publications
摘要：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 socier 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
关键词：Data mining; eigenspace; eigenvalue;event/conceptdetection; principal component; video;semantics analysis. Video Parsing and Feature Extraction;Distance-Based Data Mining.
