Confidence-based dynamic ensemble for image annotation and semantics discovery

Proceedings of the ACM International Multimedia Conference and Exhibition

Volumn , Issue , 2003, Pages 195-206

  Li, Beitao ^a   Goh, Kingshy ^a   Chang, Edward Y ^a  

^a University of California   (United States)

Author keywords

Algorithms; Performance

Indexed keywords

CONFIDENCE-BASED DYNAMIC ENSEMBLE (CDE); IMAGE DATASETS; STATISTICAL MODEL;

ALGORITHMS; DATABASE SYSTEMS; IMAGE PROCESSING; INFORMATION RETRIEVAL; MULTIMEDIA SYSTEMS; PROBABILITY; STATISTICAL METHODS; VECTORS;

SEMANTICS;

EID: 2342447244     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. E. L. Allwein, R. E. Schapire, and Y. Singer. Reducing multiclass to binary: A unifying approach for margin classifiers. Journal of Machine Learning Research, 1, 2000.
2. A. B. Benitez and S.-F. Chang. Semantic knowledge construction from annotated image collection. Proceedings of IEEE International Conference on Multimedia, August 2002.
3. D. Bouchaffra, V. Govindaraju, and S. N. Srihari. A methodology for mapping scores to probabilities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(9):923-927, 1999.
4. L. Breiman. Bagging predicators. Machine Learning, pages 123-140, 196.
5. E. Chang, K. Goh, G. Sychay, and G. Wu. Content-based soft annotation for multimodal image retrieval using bayes point machines. IEEE Trans. on Circuits and Systems for Video Technology Special Issue on Conceptual and Dynamical Aspects of Multimedia Content Description, 13(1):26-38, 2003.
6. S.-F. Chang, W. Chen, and H. Sundaram. Semantic visual templates: Linking visual features to semantics. Proceedings of IEEE International Conference on Image Processing, 1998.
7. C. K. Chow. On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory, 16(1):41-46, 1970
8. R. Collobert and S. Bengio. Svmtorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research, 1:143-160, 2001.
9. T. Dietterich and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artifical Intelligence Research, 2, 1995.
10. K. Goh, E. Chang, and K. T. Cheng. Svm binary classifier ensembles for image classification. In Proc. of ACM CIKM 2001, pages 395-402, November 2001.
11. T. Hastie and R. Tibshirani. Classification by pairwise coupling. In M. I. Jordan, M. J. Kearns, and S. A. Solla, editors, Advances in Neural Information Processing Systems, volume 10. The MIT Press, 1998.
12. X. He, W.-Y. Ma, O. King, M. Li, and H. Zhang. Learning and inferring a semantic space from user's relevance feedback for image retrieval. Proc. of ACM Multimedia, pages 343-347, December 2002.
13. T. K. Ho, J. Hull, and S. Srihari. Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(1):66-75, 1994.
14. J. Li and J. Z. Wang. Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(20), 2003.
15. P. Lipson. Context and configuration based scene classification. MIT EECS Department Phd Dissertation, September 1996.
16. M. Moreira and E. Mayoraz. Improving pairwise coupling classification with error correcting classifiers. Proceedings of the Tenth European Conference on Machine Learning, April 1998.
17. M. R. Naphade, C.-Y. Lin, J. Smith, B. Tseng, and S. Basu. Learning to annotate video databases. SPIE Electronic Imaging 2002 - Storage and Retrieval for Media Databases, January 2002.
18. J. Platt. Probabilistic outputs for svms and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press, 1999.
19. J. Platt, N. Cristianini, and J. Shawe-Taylor. Large margin dags for multiclass classification. In Advances in Neural Information Processing Systems 12, pages 547-553. MIT Press, 2000.
20. P. Poddar and P. Rao. hierarchical ensemble of neural networks. International Conference on Neural Networks, 1, 1993.
21. C. Rodriguez, J. Muguerza, M. Navarro, A. Zarate, J. Martin, and J. Perez. A two-stage classifier for broken and blurred digits in forms. International Conference on Pattern Recognition, 2:1101-1105, 1998.
22. R. F. Schapire and Y. Singer. Improved boosting algorithms using confidence-rated predictions. Proceedings of the 11th Annual Conference on Computational Learning Theory, pages 80-91, July 1998.
23. H. T. Shen, B. C. Ooi, and K. L. Tan. Giving meanings to www images. Proc. of ACM Multimedia, pages 39-48, November 2000.
24. J. R. Smith and S.-F. Chang. Multi-stage classification of images from features and related text. Proc. of the 4th DELOS Workshop, August 1997.
25. R. Srihari, Z. Zhang, and A. Rao. Intelligent indexing and semantic retrieval of multimodal documents. Information Retrieval, 2:245-275, 2000.
26. S. Tong and E. Chang. Support vector machine active learning for image retrieval. ACM International Conference on Multimedia, October 2001.
27. V. Vapnik. Estimation of Dependences Based on Empirical Data, Springer Verlag, 1982.
28. V. Vapnik. Statistical Learning Theory. Wiley, 1998.
29. J. Wang, J. Li, and G. Wiederhold. Simplicity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9):947-963, 2001.
30. J. Z. Wang and J. Li. Learning-based linguistic indexing of pictures with 2-d mhmms. Proc. of ACM Multimedia, pages 436-445, December 2002.
31. L. Wenyin, S. Dumais, Y. Sun, H. Zhang, M. Czerwinski, and B. Field. Semi-automatic image annotation. In Proc. of Interact 2001: Conference on Human-Computer Interaction, pages 326-333, Jul 2001.
32. G. Wu and E. Chang. Adaptive feature-space conformal transformation for learning imbalanced data. International Conference on Machine Learning, August 2003.
33. H. Wu, M. Li, H. Zhang, and W.-Y. Ma. Improving image retrieval with semantic classification using relevance feedback. VDB, pages 327-339, 2002.