Multilabel image classification via high-order label correlation driven active learning

IEEE Transactions on Image Processing

Volumn 23, Issue 3, 2014, Pages 1430-1441

  Zhang, Bang ^a   Wang, Yang ^a   Chen, Fang ^a  

^a CSIRO   (Australia)

Author keywords

Active learning; high order label correlation; multilabel classification

Indexed keywords

ACTIVE LEARNING; EFFICIENT LEARNING; ITERATIVE LEARNING ALGORITHMS; LABEL CORRELATIONS; LARGE-SCALE PROBLEM; LEARNING MECHANISM; MULTI-LABEL CLASSIFICATIONS; SUPERVISED MACHINE LEARNING;

ITERATIVE METHODS; MINING; SUPERVISED LEARNING;

IMAGE CLASSIFICATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; DOCUMENTATION; IMAGE ENHANCEMENT; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DOCUMENTATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED;

EID: 84897712481     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2014.2302675     Document Type: Article

References (54)

1. D. Angluin, "Queries and concept learning," Mach. Learn., vol. 2, no. 4, pp. 319-342, 1988.
2. B. Settles, "Active learning literature survey," Dept. Comput. Sci., Univ. Wisconsin-Madison, Madison, WI, USA, Tech. Rep. 1648, 2009.
3. A. Sorokin and D. Forsyth, "Utility data annotation with amazon mechanical turk," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Workshops, Jun. 2008, pp. 1-8.
4. B. Russell, A. Torralba, K. Murphy, and W. Freeman, "Labelme: A database and web-based tool for image annotation," Int. J. Comput. Vis., vol. 77, no. 1, pp. 157-173, 2008.
5. S. Kullback and R. Leibler, "On information and sufficiency," Ann. Math. Stat., vol. 22, no. 1, pp. 79-86, 1951.
6. R. Agrawal, T. Imielínski, and A. Swami, "Mining association rules between sets of items in large databases," ACM SIGMOD Rec., vol. 22, no. 2, pp. 207-216, 1993.
7. C. Campbell, N. Cristianini, and A. Smola, "Query learning with large margin classifiers," in Proc. 17th Int. Conf. Mach. Learn., 2000, pp. 111-118.
8. S. Tong and D. Koller, "Support vector machine active learning with applications to text classification," J. Mach. Learn. Res., vol. 2, pp. 45-66, Mar. 2002.
9. Y. Freund, H. Seung, E. Shamir, and N. Tishby, "Selective sampling using the query by committee algorithm," Mach. Learn., vol. 28, nos. 2-3, pp. 133-168, 1997. (Pubitemid 127506338)
10. R. Herbrich, N. D. Lawrence, and M. Seeger, "Fast sparse gaussian process methods: The informative vector machine," in Proc. Adv. Neural Inf. Process. Syst., 2002, pp. 609-616.
11. K. Brinker, "Incorporating diversity in active learning with support vector machines," in Proc. 20th Int. Conf. Mach. Learn., vol. 3. 2003, pp. 59-66.
12. Y. Guo and D. Schuurmans, "Discriminative batch mode active learning," in Proc. Adv. Neural Inf. Process., 2007, pp. 593-600.
13. S. C. Hoi, R. Jin, J. Zhu, and M. R. Lyu, "Semi-supervised SVM batch mode active learning for image retrieval," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2008, pp. 1-7.
14. S. Vijayanarasimhan, P. Jain, and K. Grauman, "Far-sighted active learning on a budget for image and video recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 3035-3042.
15. A. Krause and C. Guestrin, "Nonmyopic active learning of gaussian processes: An exploration-exploitation approach," in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 449-456.
16. S.-J. Huang, R. Jin, and Z.-H. Zhou, "Active learning by querying informative and representative examples," in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 892-900.
17. Y. Guo, "Active instance sampling via matrix partition," in Proc. Adv. Neural Inf. Process. Syst., 2010, pp. 802-810.
18. G. Schohn and D. Cohn, "Less is more: Active learning with support vector machines," in Proc. 17th Int. Conf. Mach. Learn., 2000, pp. 839-846.
19. Z. Xu, K. Yu, V. Tresp, X. Xu, and J. Wang, "Representative sampling for text classification using support vector machines," in Proc. Adv. Inf. Retr., vol. 3. 2003, pp. 393-407.
20. S. C. Hoi, R. Jin, and M. R. Lyu, "Large-scale text categorization by batch mode active learning," in Proc. 15th Int. Conf. World Wide Web, 2006, pp. 633-642. (Pubitemid 46946655)
21. S. C. H. Hoi, R. Jin, J. Zhu, and M. R. Lyu, "Batch mode active learning and its application to medical image classification," in Proc. 23rd ICML, 2006, pp. 417-424.
22. G. Qi, X. Hua, Y. Rui, J. Tang, and H. Zhang, "Two-dimensional active learning for image classification," in Proc. IEEE Conf. CVPR, Jun. 2008, pp. 1-8.
23. Y. Zhang, "Multi-task active learning with output constraints," in Proc. 24th AAAI Conf. Artif. Intell., 2010, pp. 1-6.
24. R. Schapire and Y. Singer, "BoosTexter: A boosting-based system for text categorization," Mach. Learn., vol. 39, nos. 2-3, pp. 135-168, 2000. (Pubitemid 30594821)
25. A. Elisseeff and J. Weston, "Kernel methods for multi-labelled classification and categorical regression problems," Adv. Neural Inf. Process. Syst., vol. 14. pp. 681-687, 2002.
26. K. Crammer and Y. Singer, "A new family of online algorithms for category ranking," in Proc. 25th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retr.. 2002, pp. 151-158.
27. S. Zhu, X. Ji, W. Xu, and Y. Gong, "Multi-labelled classification using maximum entropy method," in Proc. 28th Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., 2005, pp. 274-281.
28. G. Qi, X. Hua, Y. Rui, J. Tang, T. Mei, and H. Zhang, "Correlative multi-label video annotation," in Proc. 15th Int. Conf. Multimedia, 2007, pp. 17-26.
29. Y. Sun, Y. Zhang, and Z. Zhou, "Multi-label learning with weak label," in Proc. 24th AAAI Conf. Artif. Intell., 2010, pp. 593-598.
30. B. Hariharan, L. Zelnik-Manor, S. Vishwanathan, and M. Varma, "Large scale max-margin multi-label classification with priors," in Proc. 27th Int. Conf. Mach. Learn., 2010, pp. 1-8.
31. R. Yan, J. Tesic, and J. Smith, "Model-shared subspace boosting for multi-label classification," in Proc. 13th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2007, pp. 834-843. (Pubitemid 350229268)
32. S. Godbole and S. Sarawagi, "Discriminative methods for multi-labeled classification," in Proc. Adv. Knowl. Discovery Data Mining, 2004, pp. 22-30. (Pubitemid 38824880)
33. B. Zhang, Y. Wang, and W. Wang, "Batch mode active learning for multi-label image classification with informative label correlation mining," in Proc. IEEE WACV, Jan. 2012, pp. 401-407.
34. J. Shore and R. Johnson, "Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy," IEEE Trans. Inf. Theory, vol. 26, no. 1, pp. 26-37, Jan. 1980. (Pubitemid 11451543)
35. J. Goldberger, S. Gordon, and H. Greenspan, "An efficient image similarity measure based on approximations of KL-divergence between two gaussian mixtures," in Proc. 9th IEEE Int. Conf. Comput. Vis., Oct. 2003, pp. 487-493.
36. T. Hofmann, "Probabilistic latent semantic indexing," in Proc. 22nd Annu. Int. ACM SIGIR Conf. Res. Develop. Inf. Retr., 1999, pp. 50-57.
37. S. Tong and D. Koller, "Active learning for parameter estimation in Bayesian networks," in Proc. NIPS, vol. 13. 2000, pp. 647-653.
38. A. McCallum, K. Nigam et al., "Employing EM and pool-based active learning for text classification," in Proc. ICML, vol. 98. 1998, pp. 350-358.
39. Z. Xu, R. Akella, and Y. Zhang, "Incorporating diversity and density in active learning for relevance feedback," in Proc. Adv. Inf. Retr., 2007, pp. 246-257. (Pubitemid 350259555)
40. B. Zhang, G. Ye, Y. Wang, W. Wang, J. Xu, G. Herman, et al., "Multiclass graph boosting with subgraph sharing for object recognition," in Proc. 20th Int. Conf. Pattern Recognit., 2010, pp. 1541-1544.
41. B. Zhang, G. Ye, Y. Wang, J. Xu, and G. Herman, "Finding shareable informative patterns and optimal coding matrix for multiclass boosting," in Proc. IEEE 12th ICCV, Sep./Oct. 2009, pp. 56-63.
42. M. Yang, Y. Wu, and S. Lao, "Intelligent collaborative tracking by mining auxiliary objects," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 1. Jun. 2006, pp. 697-704. (Pubitemid 44931393)
43. Z. Zhou and M. Zhang, "Multi-instance multi-label learning with application to scene classification," in Proc. 20th Annu. Conf. NIPS, 2006, pp. 1-8.
44. O. Maron and A. Ratan, "Multiple-instance learning for natural scene classification," in Proc. 15th ICML, 1998, pp. 341-349.
45. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Pérez, "Solving the multiple instance problem with axis-parallel rectangles," Artif. Intell., vol. 89, no. 1, pp. 31-71, 1997. (Pubitemid 127412230)
46. G. Herman, G. Ye, J. Xu, and B. Zhang, "Region-based image categorization with reduced feature set," in Proc. IEEE 10th Workshop Multimedia Signal Process., Oct. 2008, pp. 586-591.
47. B. Zhang, Y. Wang, and W. Wang, "Multiple-instance learning from multiple perspectives: Combining models for multiple-instance learning," in Proc. IEEE WACV, Jan. 2012, pp. 481-487.
48. Z.-H. Zhou and M.-L. Zhang, "Solving multi-instance problems with classifier ensemble based on constructive clustering," Knowl. Inf. Syst., vol. 11, no. 2, pp. 155-170, 2007.
49. M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, "Learning multi-label scene classification," Pattern Recognit., vol. 37, no. 9, pp. 1757-1771, 2004. (Pubitemid 38804465)
50. D. Lewis, Y. Yang, T. Rose, and F. Li, "Rcv1: A new benchmark collection for text categorization research," J. Mach. Learn. Res., vol. 5, pp. 361-397, Dec. 2004.
51. T. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. Zheng, "NUS-WIDE: A real-world web image database from national university of singapore," in Proc. ACM Int. Conf. Image Video Retr., 2009, p. 48.
52. O. Alonso, D. E. Rose, and B. Stewart, "Crowdsourcing for relevance evaluation," ACM SigIR Forum, vol. 42, no. 2, pp. 9-15, 2008.
53. M. Lease, "On quality control and machine learning in crowdsourcing," in Proc. Human Comput., 2011, pp. 97-102.
54. M. Allahbakhsh, B. Benatallah, A. Ignjatovic, H. R. Motahari-Nezhad, E. Bertino, and S. Dustdar, "Quality control in crowdsourcing systems: Issues and directions," IEEE Internet Comput., vol. 17, no. 2, pp. 76-81, Mar./Apr. 2013.