Semantic-gap-oriented active learning for multilabel image annotation

IEEE Transactions on Image Processing

Volumn 21, Issue 4, 2012, Pages 2354-2360

  Tang, Jinhui ^a   Zha, Zheng Jun ^b   Tao, Dacheng ^c   Chua, Tat Seng ^b  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Active learning; image annotation; multilabel; semantic gap; sparse graph

Indexed keywords

ACTIVE LEARNING; IMAGE ANNOTATION; MULTI-LABEL; SEMANTIC GAP; SPARSE GRAPHS;

IMAGE ANALYSIS; LEARNING SYSTEMS;

SEMANTICS;

ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; DOCUMENTATION; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; LETTER; METHODOLOGY; SEMANTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DOCUMENTATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; PATTERN RECOGNITION, AUTOMATED; SEMANTICS; SUBTRACTION TECHNIQUE;

EID: 84859075572     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2011.2180916     Document Type: Article

References (36)

1. J. Tang, S. Yan, R. Hong, G.-J. Qi, and T.-S. Chua, "Inferring semantic concepts from community contributed images and noisy tags," in Proc. ACM Multimedia, 2009, pp. 223-232.
2. X.-J. Wang, L. Zhang, X. Li, and W.-Y. Ma, "Annotating images by mining image search results," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 11, pp. 1919-1932, Nov. 2008.
3. J. Tang, H. Li, G.-J. Qi, and T.-S. Chua, "Image annotation by graph-based inference with integrated multiple/single instance representations," IEEE Trans. Multimedia, vol. 12, no. 2, pp. 131-141, Feb. 2010.
4. T. Huang, C. Dagli, S. Rajaram, E. Chang, M. Mandel, G. Poliner, and D. Ellis, "Active learning for interactive multimedia retrieval," Proc. IEEE, vol. 96, no. 4, pp. 648-667, Apr. 2008.
5. M. Wang and X.-S. Hua, "Active learning in multimedia annotation and retrieval: A survey," ACM Trans. Intell. Syst. Technol., vol. 2, no. 2, Feb. 2011, Art. 10.
6. A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, "Content-based image retrieval at the end of the early years," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349-1380, Dec. 2000. (Pubitemid 32088465)
7. Y. Lu, L. Zhang, J. Liu, and Q. Tian, "Constructing lexica of high-level concepts with small semantic gap," IEEE Trans. Multimedia, vol. 12, no. 4, pp. 288-299, Jun. 2010.
8. J. Tang, R. Hong, S. Yan, T.-S. Chua, G.-J. Qi, and R. Jain, "Image annotation by kNN-sparse graph-based label propagation over noisilytagged web images," ACM Trans. Intell. Syst. Technol., vol. 2, no. 2, Feb. 2011, Art. 14.
9. T.-S. Chua and J. Tang et al., "NUS-WIDE: A real-world web image database from National University of Singapore," in Proc. ACM CIVR, Santorini, Greece, 2009.
10. P. Duygulu, K. Barnard, N. de Freitas, and D. Forsyth, "Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary," in Proc. Eur. Conf. Comput. Vis., 2002, pp. 97-112.
11. D. A. Cohn, Z. Ghahramani, and M. I. Jordan, "Active learning with statistical models," J. Artif. Intell. Res., vol. 4, no. 1, pp. 129-145, Jan. 1996. (Pubitemid 126646151)
12. N. Roy and A. Mccallum, "Toward optimal active learning through Monte Carlo estimation of error reduction," in Proc. Int. Conf. Mach. Learn., 2001, pp. 441-448.
13. S. Tong and E. Chang, "Support vector machine active learning for image retrieval," in Proc. ACM Multimedia, 2001, pp. 107-118. (Pubitemid 32962460)
14. J. He, M.-J. Li, H.-J. Zhang, H. Tong, and C. Zhang, "Mean version space: A new active learning method for content-based image retrieval," in Proc. Workshop Multimedia Inf. Retrieval, 2004, pp. 15-22. (Pubitemid 40390933)
15. Y.Wu, I. Kozintsev, J. yves Bouguet, and C. Dulong, "Sampling strategies for active learning in personal photo retrieval," in Proc. IEEE Int. Conf. Multimedia Expo., 2006, pp. 529-532. (Pubitemid 46679770)
16. A. Joshi, F. Porikli, and N. Papanikolopoulos, "Multi-class active learning for image classification," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2009, pp. 2372-2379.
17. 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 Int. Conf. Mach. Learn., 2006, pp. 417-424.
18. C. Dagli, S. Rajaram, and T. Huang, "Leveraging active learning for releVance feedback using an information theoretic diversity measure," in Proc. Int. Conf. Image Video Retrieval, 2006, pp. 123-132. (Pubitemid 44153552)
19. C. Zhang and T. Chen, "Annotating retrieval database with active learning," in Proc. IEEE Int. Conf. Image Process, 2003, pp. II-595-II-598.
20. H. T. Nguyen and A. Smeulders, "Active learning using pre-clustering," in Proc. 21st In. Conf. Mach. Learn., 2004, p. 79.
21. S. Ayache and G. Quénot, "Evaluation of active learning strategies for video indexing," Image Commun., vol. 22, no. 7/8, pp. 692-704, Aug. 2007. (Pubitemid 47268150)
22. P. H. Gosselin and M. Cord, "A comparison of active classification methods for content-based image retrieval," in Proc. 1st Int. Workshop CVDB, 2004, pp. 51-58.
23. M. Wang, X.-S. Hua, Y. Song, J. Tang, and L.-R. Dai, "Multiconcept multi-modality active learning for interactive video annotation," in Proc. Int. Conf. Semantic Comput., 2007, pp. 321-328.
24. G.-J. Qi, X.-S. Hua, Y. Rui, J. Tang, and H.-J. Zhang, "Two-dimensional multilabel active learning with an efficient online adaptation model for image classification," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 10, pp. 1880-1897, Oct. 2009.
25. S. Vijayanarasimhan and K. Grauman, "What's it going to cost you?: Predicting effort vs. informativeness for multi-label image annotations," in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., 2009, pp. 2262-2269.
26. M. E. Hellman and J. Raviv, "Probability of error, equivocation and the Chernoff bound," IEEE Trans. Inf. Theory, vol. IT-16, no. 4, pp. 368-372, Jul. 1970.
27. C. Elkan, "Using the triangle inequality to accelerate -means," in Proc. 20th Int. Conf. Mach. Learn., 2003, pp. 147-153.
28. R. Rao, B. Olshausen, and M. Lewicki, Probabilistic Models of the Brain: Perception and Neural Function. Cambridge, MA: MIT Press, 2002.
29. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210-227, Feb. 2009.
30. D. L. Donoho, "For most large underdetermined systems of linear equations the minimal -norm solution is also the sparsest solution," Commun. Pure Appl. Math., vol. 59, no. 6, pp. 797-829, 2006. (Pubitemid 43667226)
31. magic. [Online]. Available: http://users.ece.gatech.edu/~justin/l1magic/
32. D. Mount and S. Arya, "Ann: A library for approximate nearest neighbor searching," in Proc. CGC 2nd Annu. Fall Workshop Comput. Geom., Durham, NC, 1997.
33. J. Tang, X.-S. Hua, Wang M., Z. Gu, G.-J. Qi, and Wu X., "Correlative linear neighborhood propagation for video annotation," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 39, no. 2, pp. 409-416, Apr. 2009.
34. F. Wang and C. Zhang, "Label propagation through linear neighborhoods," IEEE Trans. Knowl. Data Eng., vol. 20, no. 1, pp. 55-67, Jan. 2008.
35. Z.-J. Zha, T. Mei, J. Wang, Z. Wang, and X.-S. Hua, "Graph-based semi-supervised learning with multiple labels," J. Vis. Commun. Image Representations, vol. 20, no. 2, pp. 97-103, Feb. 2009.
36. P. Lancaster and M. Tismenetsky, The Theory of Matrices. New York: Academic, 1987.