Correlation-based interestingness measure for video semantic concept detection
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Integration of hurricane wind analysis and multimedia semantic content analysis for public outreach
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Video semantic concept detection via associative classification
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Correlation maximisation-based discretisation for supervised classification
International Journal of Business Intelligence and Data Mining
Multimedia Databases and Data Management: A Survey
International Journal of Multimedia Data Engineering & Management
Weighted Association Rule Mining for Video Semantic Detection
International Journal of Multimedia Data Engineering & Management
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion
International Journal of Multimedia Data Engineering & Management
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Semantic concept detection has emerged as an intriguing topic in multimedia research recently. The ability to interpret high-level semantics from low-level features has been the long desired goal of many researchers. In this paper, we propose a novel framework that utilizes the ability of multiple correspondence analysis (MCA) to explore the correlation between different items (feature-value pairs) and classes (concepts) to bridge the gap between the extracted low-level features and high-level semantic concepts. Using the concepts and benchmark data identified and provided by the TRECVID project, we have shown that our proposed framework demonstrates promising results and performs better than the Decision Tree (DT),Support Vector Machine (SVM), and Naive Bayesian (NB) classifiers that are commonly applied to the TRECVID datasets.