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| Lin Lin, 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. | |||
| BibTex | x | ||
| @article{ 10.1109/MMUL.2011.35, author = {Lin Lin and Chao Chen and Mei-Ling Shyu and Shu-Ching Chen}, title = {Weighted Subspace Filtering and Ranking Algorithms for Video Concept Retrieval}, journal ={IEEE Multimedia}, volume = {18}, number = {3}, issn = {1070-986X}, year = {2011}, pages = {32-43}, doi = {http://doi.ieeecomputersociety.org/10.1109/MMUL.2011.35}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - MGZN JO - IEEE Multimedia TI - Weighted Subspace Filtering and Ranking Algorithms for Video Concept Retrieval IS - 3 SN - 1070-986X SP32 EP43 EPD - 32-43 A1 - Lin Lin, A1 - Chao Chen, A1 - Mei-Ling Shyu, A1 - Shu-Ching Chen, PY - 2011 KW - concept retrieval KW - multiple correspondence analysis (MCA) KW - filtering KW - dissimilarity measure KW - ranking VL - 18 JA - IEEE Multimedia ER - | |||
1. N.J. Salkind ed., Encyclopedia of Measurement and Statistics, Sage Publications, 2007.
2. A.F. Smeaton, P. Over, and W. Kraaij, "Evaluation Campaigns and Trecvid," ACM Int'l Workshop Multimedia Information Retrieval (MIR), ACM Press, 2006, pp. 321-330.
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.
1. I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. Morgan Kaufmann, 2005.
2. T. Koreniusa, J. Laurikkalaa, and M. Juhola, "On Principal Component Analysis, Cosine, and Euclidean Measures in Information Retrieval," Information Sciences, vol. 177, no. 22, 2007, pp. 4893-4905.
3. T. Joachims, "Optimizing Search Engines Using Clickthrough Data," Proc. 8th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, ACM Press, 2002, pp. 133-142.
4. C. Burges, "Learning to Rank Using Gradient Descent," Proc. Int'l Conf. Machine Learning, ACM Press, 2005, pp. 86-96.
5. Y. Freund et al., "An Efficient Boosting Algorithm for Combining Preference," J. Machine Learning Research, vol. 4, no. 6, 2003, pp. 933-963.
6. R. Yan and A.G. Hauptmann, "Efficient Margin-Based Rank Learning Algorithm for Information Retrieval," Proc. ACM Int'l Conf. Image and Video Retrieval (CIVR), ACM Press, 2006, pp. 113-121.
7. T.-S. Chua et al., "Trecvid 2005 by NUS PRIS," Proc. NIST Trecvid-2005, NIST, 2005.
8. Y. Liu et al., "Graph-Based Pairwise Learning to Rank for Video Search," Proc. Int'l Multimedia Modeling Conf. Advances, vol. 5371, Springer-Verlag, 2009, pp. 175-184.
9. S.C.H. Hoi and M.R. Lyu, "A Multimodal and Multilevel Ranking Scheme for Large-Scale Video Retrieval," IEEE Trans. Multimedia, vol. 10, no. 4, 2008, pp. 607-619.

