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Issue No.03 - July-September (2011 vol.18)
pp: 32-43
Chao Chen , University of Miami, Coral Gables
Mei-Ling Shyu , University of Miami , Coral Gables
Shu-Ching Chen , Florida International University , Miami
ABSTRACT
The proposed framework, with weighted subspace filtering and ranking components, is the first attempt in multimedia research to apply multiple correspondence analysis to selected features while pruning data instances.
INDEX TERMS
concept retrieval, multiple correspondence analysis (MCA), filtering, dissimilarity measure, ranking
CITATION
Chao Chen, Mei-Ling Shyu, Shu-Ching Chen, "Weighted Subspace Filtering and Ranking Algorithms for Video Concept Retrieval", IEEE MultiMedia, vol.18, no. 3, pp. 32-43, July-September 2011, doi:10.1109/MMUL.2011.35
REFERENCES
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3. L. Lin et al., "Effective Feature Space Reduction with Imbalanced Data for Semantic Concept Detection," Proc. IEEE Int'l Conf. Sensor Networks, Ubiquitous, and Trustworthy Computing, IEEE CS Press, 2008, pp. 262-269.
4. S.-C. Chen, M.-L. Shyu, and M. Chen, "An Effective Multi-Concept Classifier for Video Streams," Proc. IEEE Int'l Conf. Semantic Computing (ICSC), IEEE CS Press, 2008, pp. 80-87.
5. I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan Kaufmann, 2005.
6. Y.-G. Jiang et al., "Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study," IEEE Trans. Multimedia, vol. 12, no. 1, 2010, pp. 42-53.
7. A. Yanagawa et al., Columbia University's Baseline Detectors for 374 LSCOM Semantic Visual Concepts, Advent tech. report 222-2006-8, Columbia University, Mar. 2007.
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