Semisupervised SVM batch mode active learning with applications to image retrieval

ACM Transactions on Information Systems

Volumn 27, Issue 3, 2009, Pages

  Hoi, Steven C H ^a   Jin, Rong ^b   Zhu, Jianke ^c   Lyu, Michael R ^c  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b Michigan State University   (United States)

^c CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Active learning; Batch mode active learning; Content based image retrieval; Human computer interaction; Semisupervised learning; Support vector machines

Indexed keywords

ACTIVE LEARNING; ACTIVE LEARNING METHODS; BATCH MODES; CONTENT-BASED IMAGE RETRIEVAL; CONVENTIONAL APPROACH; GREEDY ALGORITHMS; KERNEL FUNCTION; LABELED AND UNLABELED DATA; MEDICAL IMAGES; MIN-MAX; NOVEL ALGORITHM; PHOTO IMAGES; POOR PERFORMANCE; QUADRATIC PROGRAMS; RELEVANCE FEEDBACK; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; STATE-OF-THE-ART APPROACH; SUBMODULAR FUNCTIONS; TRAINING DATA;

ALGORITHMS; COMBINATORIAL OPTIMIZATION; CONTENT BASED RETRIEVAL; FEATURE EXTRACTION; FEEDBACK; GEARS; HUMAN COMPUTER INTERACTION; INFORMATION RETRIEVAL; KNOWLEDGE MANAGEMENT; MULTILAYER NEURAL NETWORKS;

SUPPORT VECTOR MACHINES;

EID: 68549086546     PISSN: 10468188     EISSN: 15582868     Source Type: Journal    
DOI: 10.1145/1508850.1508854     Document Type: Article

References (48)

1. BOYD, S. AND VANDENBERGHE, L. 2004. Convex Optimization. Cambridge University Press, Cambridge, U.K.
2. BRINKER, K. 2003. Incorporating diversity in active learning with support vector machines. In Proceedings of the International Conference on Machine Learning (ICML).
3. CHAPELLE, O., SCHöLKOPF, B., AND ZIEN, A. 2006. Semisupervised Learning. MIT Press, Cambridge,
4. MA. COHN, D. A., GHAHRAMANI, Z., AND JORDAN, M. I. 1995. Active learning with statistical models. In Proceedings of the NIPS.
5. DAGLI, C. K., RAJARAM, S., AND HUANG, T. S. 2006. Leveraging active learning for relevance feedback using an information theoretic diversity measure. In Proceedings of the ACM Conferece on Image and Video Retrieval (CIVR), Lecture Notes in Computer Science, vol.4071. Springer, Berlin, Germany, 123-132.
6. GOH, K.-S., CHANG, E. Y., AND LAI, W.-C. 2004. Multimodal concept-dependent active learning for image retrieval. In MULTIMEDIA '04: Proceedings of the 12th Annual ACM International Conference on Multimedia. ACM, New York, NY, 564-571.
7. HOI, S. C., JIN, R., ZHU, J., AND LYU, M. R. 2006a. Batch mode active learning and its application to medical image classification. In Proceedings of the 23rd International Conference on Machine Learning (ICML), Pittsburgh, PA.
8. HOI, S. C. AND LYU, M. R. 2004. A novel log-based relevance feedback technique in content-based image retrieval. In Proceedings of the ACM International Conference on Multimedia (MM). New York, NY, 24-31.
9. HOI, S. C. H., JIN, R., AND LYU, M. R. 2006b. Large-scale text categorization by batch mode active learning. In Proceedings of the 15th International Conference on the World Wide Web (WWW, Edinburgh, Scotland). 633-642.
10. HOI, S. C. H. AND LYU, M. R. 2005. A semisupervised active learning framework for image retrieval. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR). 302-309.
11. HOI, S. C. H., LYU, M. R., AND CHANG, E. Y. 2006c. Learning the unified kernel machines for classification. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD, Philadelphia, PA). 187-196.
12. HOI, S. C. H., LYU, M. R., AND JIN, R. 2006d. A unified log-based relevance feedback scheme for image retrieval. IEEE Trans. Knowl. Data Eng. 18, 4, 509-1204
13. HONG, P., TIAN, Q., AND HUANG, T. S. 2000. Incorporate support vector machines to content-based image retrieval with relevant feedback. In Proceedings of the IEEE International Conference on Image Processing (ICIP). Vol.3. 750-753.
14. HUANG, T. S. AND ZHOU, X. S. 2001. Image retrieval by relevance feedback: From heuristic weight adjustment to optimal learning methods. In Proceedings of the IEEE International Conference on Image Processing (ICIP). Vol.3. 2-5.
15. ISHIKAWA, Y., SUBRAMANYA, R., AND FALOUTSOS, C. 1998. MindReader: Querying databases through multiple examples. In Proceedings of the 24th International Conference on Very Large Data Bases (VLDB). 218-227.
16. KING, I. AND ZHONG, J. 2003. Integrated probability function and its application to content-based image retrieval by relevance feedback. Patt. Recog. 36, 9, 2177-2186.
17. LAAKSONEN, J., KOSKELA, M., AND OJA, E. 1999. Picsom: Self-organizing maps for content-based image retrieval. InProceedings of the International Joint Conferenceon Neural Networks(IJCNN, Washington, DC).
18. LEW, M. S., SEBE, N., DJERABA, C., AND JAIN, R. 2006. Content-Based multimedia information retrieval: State of the art and challenges. ACM Trans. Multimed. Comput. Commun. Appl. 2, 1, 1-19.
19. LI, J., ALLINSON, N., TAO, D., AND LI, X. 2006. Multitraining support vector machine for image retrieval. IEEE Trans. Image Process. 15, 11, 3597-3601. (Pubitemid 44615153)
20. LIERE, R. AND TADEPALLI, P. 1997. Active learning with committees for text categorization. In Proceedings of AAAI.
21. MACARTHUR, S., BRODLEY, C., AND SHYU, C. 2000. Relevance feedback decision trees in content-based image retrieval. In Proceedings of the IEEE Workshop on Content-Based Access of lmage and Video Libraries. 68-72.
22. MANJUNATH, B. S. AND MA, W. Y. 1996. Texture features for browsing and retrieval of image data. IEEE Trans. Patt. Anal. Mach. Intell. 18, 8, 837-842.
23. MCCALLUM, A. K. AND NIGAM, K. 1998. Employing EM and pool-based active learning for text classification. In Proceedings of the International Conference on Machine Learning (ICML).
24. MULLER, H., DESELAERS, T., LEHMANN, T., CLOUGH, P., AND HERSH, W. 2007. Overview of the image-clefmed 2006 medical retrieval annotation tasks. In Proceedings of the 7th Workshop of Cross-Language Evaluation Forum (CLEF).
25. NEMHAUSER, G., WOLSEY, L., AND FISHER, M. 1978. An analysis of the approximations for maximizing submodular set functions. Math. Program. 14, 265-294.
26. PANDA, N., GOH, K.-S., ANDCHANG, E. Y. 2006. Active learning in very large databases. J. Multimed. Tools Appl. (Special Issue on Computer Vision Meets Databases) 31, 3, 249-267.
27. QIAN, F., LI, M., ZHANG, L., ZHANG, H.-J., AND ZHANG, B. 2002. Gaussian mixture model for relevance feedback in image retrieval. In Proceedings of the International Conference on Multimedia and Expo (ICME). Vol.1. 229-232.
28. RUI, Y., HUANG, T., AND MEHROTRA, S. 1997. Content-based image retrieval with relevance feedback in mars. II. In Proceedings of the IEEE International Conference on Image Processing. 815-818.
29. RUI, Y., HUANG, T. S., ORTEGA, M., AND MEHROTRA, S. 1998. Relevance feedback: A power tool in interactive content-based image retrieval. IEEE Trans. Circ. Syst. Vid. Tech. 8, 5 (Sept.), 644-655. (Pubitemid 128743730)
30. SCHOHN, G. AND COHN, D. 2000. Less is more: Active learning with support vector machines. In Proceedings of the 17th International Conference on Machine Learning (ICML).
31. SINDHWANI, V., NIYOGI, P., AND BELKIN, M. 2005. Beyond the point cloud: From transductive to semisupervised learning. In Proceedings of the International Conference on Machine Learning (ICML).
32. SMEULDERS, A. W. M., WORRING, M., SANTINI, S., GUPTA, A., AND JAIN, R. 2000. Content-based image retrieval at the end of the early years. IEEE Trans. Patt. Anal. Mach. Intell. 22, 12, 1349-1380.
33. TAO, D., LI, X., AND MAYBANK, S. 2007. Negative samples analysis in relevance feedback. IEEE Trans. Image Process. 19, 4, 568-580.
34. TAO, D. AND TANG, X. 2004a. Nonparametric discriminant analysis in relevance feedback for content-based image retrieval. In Proceedings of the IEEE International Conference on Pattern Recognition (ICPR). 1013-1016.
35. TAO, D. AND TANG, X. 2004b. Random sampling based svm for relevance feedback image retrieval. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).
36. TAO, D., TANG, X., LI, X., AND RUI, Y. 2006. Direct kernel biased discriminant analysis: A new content-based image retrieval relevance feedback algorithm. IEEE Trans. Image Process. 8, 4, 716-727.
37. TIEU, K. AND VIOLA, P. 2000. Boosting image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Vol.1. 228-235.
38. TONG, S. AND CHANG, E. 2001. Support vector machine active learning for image retrieval. In Proceedings of the ACM Multimedia Conference.
39. TONG, S. AND KOLLER, D. 2000. Support vector machine active learning with applications to text classification. In Proceedings of the 17th International Conference on Machine Learning (ICML).
40. VAPNIK, V. N. 1998. Statistical Learning Theory. Wiley, New York, NY. VASCONCELOS, N. AND LIPPMAN, A. 1999. Learning from user feedback in image retrieval systems. In Proceedings of NIPS.
41. WANG, L., CHAN, K. L., AND ZHANG, Z. 2003. Bootstrapping SVM active learning by incorporating unlabelled images for image retrieval. In Proceedings of the CVPR.
42. WU, Y., TIAN, Q., AND HUANG, T. S. 2000. Discriminant-EM algorithm with application to image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
43. YAN, R., HAUPTMANN, A., AND JIN, R. 2003. Negative pseudo-relevance feedback in content-based video retrieval. In Proceedings of ACM Multimedia (MM, Berkeley, CA).
44. YUHONG GUO, D. S. 2007. Discriminative batch mode active learning. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).
45. ZHANG, L., LIN, F., AND ZHANG, B. 2001. Support vector machine learning for image retrieval. In Proceedings of the International Conference on Image Processing (ICIP). Vol.2. 721-724. (Pubitemid 33057807)
46. ZHANG, T. AND ANDO, R. K. 2005. Analysis of spectral kernel design based semisupervised learning. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS).
47. ZHOU, X. S. AND HUANG, T. S. 2001. Small sample learning during multimedia retrieval using biasmap. In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR).
48. ZHOU, X. S. AND HUANG, T. S. 2003. Relevance feedback for image retrieval: a comprehensive review. ACM Multimed. Syst. 8, 6, 536-544.