Query-based video event definition using rough set theory
EiMM '09 Proceedings of the 1st ACM international workshop on Events in multimedia
Multiple correspondence analysis for "tall" data sets
Intelligent Data Analysis
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
Classification of high dimensional and imbalanced hyperspectral imagery data
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Weighted Association Rule Mining for Video Semantic Detection
International Journal of Multimedia Data Engineering & Management
International Journal of Multimedia Data Engineering & Management
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Semantic understanding of multimedia content has become a very popular research topic in recent years. Semantic concept detection algorithms face many challenges such as the semantic gap and imbalance data, among others. In this paper, we propose a novel algorithm using multiple correspondence analysis (MCA) to discover the correlation between features and classes to reduce the feature space and to bridge the semantic gap. Moreover, the proposed algorithm is able to explore the correlation between items (i.e., feature-value pairs generated for each of the features) and classes which expands its ability to handle imbalance data sets. To evaluate the proposed algorithm, we compare its performance on semantic concept detection with several existing feature selection methods under various well-known classifiers using some of the concepts and benchmark data available from the TRECVID project. The results demonstrate that our proposed algorithm achieves promising performance, and it performs significantly better than those feature selection methods in the comparison for the imbalanced data sets.