A framework for classifier adaptation for large-scale multimedia data

Proceedings of the IEEE

Volumn 100, Issue 9, 2012, Pages 2639-2657

  Yang, Jun ^a   Tong, Wei ^b   Hauptmann, Alexander G ^b  

^a FACEBOOK   (United States)

^b Carnegie Mellon University   (United States)

Author keywords

Adaptive SVMs; cross domain learning; model adaptation

Indexed keywords

SEMANTICS;

ADAPTATION ALGORITHMS; ADAPTIVE SVMS; CROSS-DOMAIN LEARNING; MACHINE LEARNING TECHNIQUES; MODEL ADAPTATION; MULTIPLE CLASSIFIERS; REGULARIZATION FUNCTION; SUPERVISED CLASSIFIERS;

SUPPORT VECTOR MACHINES;

EID: 84865444497     PISSN: 00189219     EISSN: None     Source Type: Journal    
DOI: 10.1109/JPROC.2012.2204009     Document Type: Article

References (48)

1. A. Amir, M. Berg, S.-F. Chang, W. Hsu, G. Iyengar, C.-Y. Lin, M. Naphade, A. Natsev, C. Neti, H. Nock, J. R. Smith, B. Tseng, Y. Wu, and D. Zhang, "IBM research TRECVID-2003 video retrieval system," in Proc. TRECVID Video Retrieval Evauation Workshop, NIST, Gaithersburg, MD, Dec. 2003. DOI: 10.1.1.94. 4110. [Online]. Available: http://www-nlpir. nist.gov/projects/ tvpubs/tvpapers03/ibm. smith.paper.final2.pdf
2. C. Campbell, N. Cristianini, and A. Smola, "Query learning with large margin classifiers," in Proc. Int. Conf. Mach. Learn., 2000, pp. 111-118.
3. R. Caruana, "Multitask learning," Mach. Learn., vol. 28, no. 1, pp. 41-75, 1997. (Pubitemid 127507169)
4. G. Cauwenberghs and T. Poggio, "Incremental and decremental support vector machine learning," in Proc. Neural Inf. Process. Syst., 2000, pp. 409-415.
5. S. Chang, W. Jiang, A. Yanagawa, and E. Zavesky, "Columbia University TRECVID 2007 high-level feature extraction," in Proc. TRECVID Video Retrieval Evaluation Workshop, NIST, Gaithersburg, MD, Nov. 2007. [Online]. Available: http://www-nlpir.nist. gov/projects/tvpubs/tv7.papers/ columbia.pdf
6. S. F. Chang, W. Hsu, L. Kennedy, L. Xie, A. Yanagawa, E. Zavesky, and D. Zhang, "Columbia University TRECVID-2005 video search and high-level feature extraction," in Proc. TRECVID Video Retrieval Evauation Workshop, NIST, Gaithersburg, MD, Nov. 2005. [Online]. Available: http://www-nlpir.nist. gov/projects/tvpubs/tv5. papers/columbia.pdf
7. O. Chapelle, B. Scholkopf, and A. Zien, Semi-Supervised Learning. Cambridge, MA: MIT Press, 2006.
8. M.-Y. Chen and A. Hauptmann, "Discriminative fields for modeling semantic concepts in video," in Proc. 8th Conf. Large-Scale Semantic Access to Content, 2007, pp. 151-166.
9. I. Cox, M. Miller, S. Omohundro, and P. Yianilos, "PicHunter: Bayesian relevance feedback for image retrieval," in Proc. 13th Int. Conf. Pattern Recognit., 1996, vol. 3, pp. 361-369.
10. W. Dai, Q. Yang, G.-R. Xue, and Y. Y, "Boosting for transfer learning," in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 193-200.
11. S. B. David, J. Blitzer, K. Crammer, and F. Pereira, "Analysis of representations for domain adaptation Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press, 2007, pp. 137-144, DOI: 10.1.1.137.7569. [Online]. Available: http://books.nips.cc/papers/files/nips20/NIPS200 7-0628.pdf
12. L. Duan, I. W. Tsang, D. Xu, and S. J. Maybank, "Domain transfer SVM for video concept detection," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2009, pp. 1375-1381.
13. L. Duan, D. Xu, I. W. Tsang, and J. Luo, "Visual event recognition in videos by learning from web data," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2010, pp. 1959-1966.
14. R. Gopalan, R. Li, and R. Chellappa, "Domain adaptation for object recognition: An unsupervised approach," in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 999-1006.
15. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, Prediction. New York: Springer-Verlag, 2001.
16. W. Jiang, E. Zavesky, S. Chang, and A. Loui, "Crossdomain learning methods for high-level visual concept classification," in Proc. IEEE Int. Conf. Image Process., 2008, pp. 161-164.
17. T. Joachims, "Making large-scale support vector machine learning practical," in Advances in Kernel Methods: Support Vector Machines. New York: Springer-Verlag, 1998.
18. L. S. Kennedy, A. P. Natsev, and S.-F. Chang, "Automatic discovery of query-class-dependent models for multimodal search," in Proc. 13th Annu. ACM Int. Conf. Multimedia, 2005, pp. 882-891.
19. R. Klinkenberg and T. Joachims, "Detecting concept drift with support vector machines," in Proc. Int. Conf. Mach. Learn., 2000, pp. 487-494.
20. B. Kulis, K. Saenko, and T. Darrell, "What you saw is not what you get: Domain adaptation using asymmetric kernel transforms," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2011, pp. 1785-1792.
21. H. Li, Y. Shi, M. yu Chen, A. G. Hauptmann, and Z. Xiong, "Hybrid active learning for cross-domain video concept detection," in Proc. Int. Conf. Multimedia, 2010, pp. 1003-1006.
22. X. Liao, Y. Xue, and L. Carin, "Logistic regression with an auxiliary data source," in Proc. Int. Conf. Mach. Learn., 2005, pp. 505-512. (Pubitemid 43183372)
23. Z. Marx, M. Rosenstein, L. Kaelbling, and T. Dietterich, "Transfer learning with an ensemble of background tasks," in NIPS 2005 Workshop on Inductive Transfer: 10 Years Later, Whistler, BC, Canada, Dec. 2005. DOI: 10.1.1.120.4227. [Online]. Available: http://iitrl.acadiau.ca/itws05/Papers/ ITWS19-MarxRosensteinKaelbling Dietterich-REV.pdf
24. M. R. Naphade, L. Kennedy, J. R. Kender, S. F. Chang, J. Smith, P. Over, and A. Hauptmann, "A light scale concept ontology for multimedia understanding for TRECVID 2005, IBM Res. Tech. Rep., 2005.
25. M. R. Naphade, T. Kristjansson, B. Frey, and T. Huang, "Probabilistic multimedia objects Multijects: A novel approach to video indexing and retrieval in multimedia systems," in Proc. Int. Conf. Image Process., 1998, vol. 3, pp. 536-540.
26. A. P. Natsev, M. R. Naphade, and J. R. Smith, "Semantic representation: Search and mining of multimedia content," in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Disc. Data Mining, 2004, pp. 641-646. (Pubitemid 40114984)
27. S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.
28. C. Petersohn, "Fraunhofer HHI at TRECVID 2004: Shot boundary detection system," in Proc. TREC Video Retrieval Evaluation, 2004. DOI: 10.1.1.118.6497. [Online]. Available: http://www-nlpir. nist.gov/projects/ tvpubs/tvpapers04/fraunhofer.pdf
29. J. Platt, "Fast training of support vector machines using sequential minimal optimization Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press, 1999, pp. 185-208.
30. G.-J. Qi, X.-S. Hua, Y. Rui, J. Tang, T. Mei, and H.-J. Zhang, "Correlative multi-label video annotation," in Proc. 15th ACM Int. Conf. Multimedia, 2007, pp. 17-26.
31. G. Quenot and S. Ayache, TRECVID 2007 Collaborative Annotation. [Online]. Available: http://mrim.imag.fr/tvca/
32. Y. Rui, T. Huang, M. Ortega, and S. Mehrotra, "Relevance feedback: A power tool for interactive content-based image retrieval," IEEE Trans. Circuits Syst. Video Technol., vol. 8, no. 5, pp. 644-655, Sep. 1998. (Pubitemid 128743730)
33. A. F. Smeaton, P. Over, and W. Kraaij, "Evaluation campaigns and TRECVID," in Proc. 8th ACM Int. Workshop Multimedia Inf. Retrieval, 2006, pp. 321-330. (Pubitemid 47168230)
34. C. Snoek, M. Worring, J. Geusebroek, D. Koelma, and F. Seinstra, "The MediaMill TRECVID 2004 semantic video search engine," in Proc. TREC Video Retrieval Evaluation Workshop, NIST, Gaithersburg, MD, Nov. 2004. DOI: 10.1.1.80.7524. [Online].Available: http://www-nlpir.nist.gov/projects/ tvpubs/tvpapers04/mediamill.pdf
35. W. N. Street and Y. Kim, "A streaming ensemble algorithm (SEA) for large-scale classification," in Proc. ACM SIGKDD Int. Conf. Knowl. Disc. Data Mining, 2001, pp. 377-382.
36. N. A. Syed, H. Liu, and K. K. Suang, "Incremental learning with support vector machines," in Proc. Workshop Support Vector Mach./Int. Joint Conf. Artif. Intell., San Diego, CA, 1999. DOI: 10.1.1.46.6367.
37. S. Tong and D. Koller, "Support vector machine active learning with applications to text classification," in Proc. Int. Conf. Mach. Learn., 2000, pp. 999-1006.
38. H. Wang, W. Fan, P. S. Yu, and J. Han, "Mining concept-drifting data streams using ensemble classifiers," in Proc. ACM SIGKDD Int. Conf. Knowl. Disc. Data Mining, 2003, pp. 226-235.
39. P. Wu and T. G. Dietterich, "Improving SVM accuracy by training on auxiliary data sources," in Proc. Int. Conf. Mach. Learn., 2004, p. 110.
40. Y. Wu, E. Y. Chang, K. C.-C. Chang, and J. R. Smith, 'Optimal multimodal fusion for multimedia data analysis," in Proc. 12th Annu. ACM Int. Conf. Multimedia, 2004, pp. 572-579. (Pubitemid 40211831)
41. Y. Wu, B. L. Tseng, and J. R. Smith, 'Ontology-based multiclassification learning for video concept detection," in Proc. IEEE Int. Conf. Multimedia Expo, 2004, vol. 2, pp. 1003-1006. (Pubitemid 40063206)
42. R. Yan, J. Yang, and A. G. Hauptmann, "Learning query-class dependent weights in automatic video retrieval," in Proc. 12th ACM Int. Conf. Multimedia, 2004, pp. 548-555. (Pubitemid 40211829)
43. R. Yan, M. yu Chen, and A. G. Hauptmann, "Mining relationship between video concepts using probabilistic graphical model," in Proc. IEEE Int. Conf. Multimedia Expo, 2006, pp. 301-304. (Pubitemid 46679714)
44. J. Yang and A. Hauptmann, "(Un)reliability of video concept detection," in Proc. Int. Conf. Content-Based Image Video Retrieval, 2008, pp. 85-94.
45. J. Yang, R. Yan, and A. G. Hauptmann, "Cross-domain video concept detection using adaptive SVMs," in Proc. Int. Conf. Multimedia, 2007, pp. 188-197.
46. Y. Yang, Y. Yang, Z. Huang, and H. T. Shen, "Transfer tagging from image to video," in Proc. Int. Conf. Multimedia, 2011 1137-1140.
47. Y. Yao and G. Doretto, "Boosting for transfer learning with multiple sources," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2010, pp. 1855-1862.
48. J. Zhang, Z. Ghahramani, and Y. Yang, "Learning multiple related tasks using latent independent component analysis," Adv. Neural Inf. Process. Syst., vol. 18, pp. 1585-1592, 2006.