Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework
Mei-Ling Shyu
Zongxing Xie
Min Chen
Shu-Ching Chen
Univ. of Miami, Coral Gables;
This paper appears in: Multimedia, IEEE Transactions on
Publication Date: Feb. 2008
Volume: 10,
Issue: 2
On page(s): 252-259
ISSN: 1520-9210
INSPEC Accession Number: 9748281
Digital Object Identifier: 10.1109/TMM.2007.911830
Current Version Published: 2008-01-16
Abstract
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.
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