Cooperative sparse representation in two opposite directions for semi-supervised image annotation

IEEE Transactions on Image Processing

Volumn 21, Issue 9, 2012, Pages 4218-4231

  Zhao, Zhong Qiu ^a   Glotin, Hervé ^b   Xie, Zhao ^a   Gao, Jun ^a   Wu, Xindong ^a,c  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

^b CNRS   (France)

^c University of Vermont   (United States)

Author keywords

Co training; image annotation; image retrieval; semi supervised learning; sparse representation (SR)

Indexed keywords

CO-TRAINING; COMPARATIVE EXPERIMENTS; COMPUTER VISION PROBLEMS; GAUSSIAN FIELD; GLOBAL CONSISTENCY; IMAGE ANNOTATION; IMAGE REPRESENTATIONS; KERNEL SPACE; LABELED IMAGES; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING; SEMI-SUPERVISED METHOD; SPARSE REPRESENTATION; TRAINING IMAGE; TRANSDUCTIVE SUPPORT VECTOR MACHINE;

COMPUTER VISION; CONTENT BASED RETRIEVAL; HARMONIC FUNCTIONS; IMAGE ANALYSIS; IMAGE RETRIEVAL; SUPERVISED LEARNING;

IMAGE CLASSIFICATION;

EID: 84865438000     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2197631     Document Type: Article

References (41)

1. R. Datta, D. Joshi, J. Li, and J. Z. Wang, "Image retrieval: Ideas, influences, and trends of the new age," ACM Comput. Surv., vol. 40, no. 2, pp. 1-60, 2008.
2. J. Li and J. Wang, "Real-time computerized annotation of pictures," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 6, pp. 985-1002, Jun. 2008. (Pubitemid 351650103)
3. J. Liu, M. Li, Q. Liu, H. Lu, and S. Ma, "Image annotation via graph learning," Pattern Recognit., vol. 42, no. 2, pp. 218-228, 2009.
4. C. Wang, S. Yan, L. Zhang, and H. Zhang, "Multi-label sparse coding for automatic image annotation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2009, pp. 1643-1650.
5. Z. Zhao and H. Glotin, "Diversifying image retrieval by affinity propagation clustering on visual manifolds," IEEE Multimedia, vol. 16, no. 4, pp. 34-43, Oct. 2009.
6. S. Zhang, J. Huang, Y. Huang, Y. Yu, H. Li, and D. N. Metaxas, "Automatic image annotation using group sparsity," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 3312-3319.
7. Y. F. Zhao, Y. Zhao, and Z. Zhu, "TSVM-HMM: Transductive SVM based hidden Markov model for automatic image annotation," Expert Syst. Appl., vol. 36, no. 6, pp. 9813-9818, 2009.
8. D. Zhou, O. Bousquet, T. N. Lal, J.Weston, and B. Scholkopf, "Learning with local and global consistency," in Advances in Neural Information Processing Systems 16. Cambridge, MA: MIT Press, 2004, pp. 321-328.
9. J. He, M. Li, H.-J. Zhang, H. Tong, and C. Zhang, "Generalized manifold-ranking-based image retrieval," IEEE Trans. Image Process., vol. 15, no. 10, pp. 3170-3177, Oct. 2006. (Pubitemid 44473655)
10. X. Zhu, Z. Ghahramani, and J. Lafferty, "Semi-supervised learning using Gaussian fields and harmonic functions," in Proc. 20th Int. Conf. Mach. Learn., 2003, pp. 912-919.
11. M. Wang, X. S. Hua, R. Hong, J. Tang, G. J. Qi, and Y. Song, "Unified video annotation via multi-graph learning," IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 5, pp. 733-746, May 2009.
12. C. Domeniconi, J. Peng, and B. Yan, "Composite kernels for semisupervised clustering," Knowl. Inf. Syst., vol. 28, no. 1, pp. 99-116, 2011.
13. J. Wright, A. Yang, A. Ganesh, S. 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.
14. J. Marial, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, "Non-local sparse models for image restoration," in Proc. IEEE 12th Int. Conf. Comput. Vis., Sep.-Oct. 2009, pp. 2272-2279.
15. S. Gao, I. W.-H. Tsang, and L.-T. Chia, "Kernel sparse representation for image classification and face recognition," in Proc. 11th Eur. Conf. Comput. Vis., 2010, pp. 1-14.
16. J. Yang, K. Yu, Y. Gong, and T. Huang, "Linear spatial pyramid matching using sparse coding for image classification," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2009, pp. 1794-1801.
17. J. Gui, Z. Sun, W. Jia, R. Hu, Y. Lei, and S. Ji, "Discriminant sparse neighborhood preserving embedding for face recognition," Pattern Recognit., vol. 45, no. 8, pp. 2884-2893, 2012.
18. T. Joachims, "Transductive inference for text classification using support vector machines," in Proc. 16th Int. Conf. Mach. Learn., 1999, pp. 200-209.
19. A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, "Toward a practical face recognition system: Robust alignment and illumination by sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 2, pp. 372-386, Feb. 2012.
20. S. Gao, I. W.-H. Tsang, L.-T. Chia, and P. Zhao, "Local features are not lonely-Laplacian sparse coding for image classification," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 3555-3561.
21. X. Yuan and S. Yan, "Visual classification with multi-task joint sparse representation," in Proc. IEEE Comput. Vis. Pattern Recognit., Jun. 2010, pp. 3493-3500.
22. S. S. Chen, D. L. Donoho, and M. A. Saunders, "Atomic decomposition by basis pursuit," SIAM Rev., vol. 43, no. 1, pp. 129-159, 2001. (Pubitemid 32406896)
23. D. L. Donoho and Y. Tsaig, "Fast solution of 1-norm minimization problems when the solution may be sparse," IEEE Trans. Inf. Theory, vol. 54, no. 11, pp. 4789-4812, Nov. 2008.
24. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training," in Proc. Workshop Comput. Learn. Theory, 1998, pp. 92-100.
25. M.-A. Krogel and T. Scheffer, "Multi-relational learning, text mining, and semi-supervised learning for functional genomics," Mach. Learn., vol. 57, nos. 1-2, pp. 61-81, 2004.
26. B. Scholkopf, A. Smola, and K. Muller, "Kernel principal component analysis," in Proc. Int. Conf. Artif. Neural Netw., 1997, pp. 583-588. (Pubitemid 127140297)
27. Y. Yan, L. Chen, and W.-C. Tjhi, "Semi-supervised fuzzy co-clustering algorithm for document categorization," Knowl. Inf. Syst., DOI: 10.1007/s10115-011-0454-9, 2011.
28. S. Abney, "Bootstrapping," in Proc. 40th Annu. Meeting Assoc. Comput. Linguist., 2002, pp. 360-367.
29. M. S. Asif and J. Romberg, "Dynamic updating for 1 minimization," IEEE J. Sel. Topics Signal Process., vol. 4, no. 2, pp. 421-434, Apr. 2010.
30. D. Thomas and H. Allan, "The visual concept detection task in imageCLEF08," in Evaluating Systems for Multilingual and Multimodal Information Access. New York: Springer-Verlag, 2008.
31. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. (2010). The PASCAL Visual Object Classes Challenge Results [Online]. Available: http://www.pascal-network.org/challenges/VOC/voc2010/workshop/index.html
32. M. Swain and D. Ballard, "Color indexing," Int. J. Comput. Vis., vol. 7, no. 1, pp. 11-32, 1991. (Pubitemid 23621975)
33. I. Daubechies, Ten Lectures on Wavelets (Applied Mathematics). Philadelphia, PA: SIAM, 1992.
34. D. Lowe, "Object recognition from local scale-invariant features," in Proc. Int. Conf. Comput. Vis., vol. 2. 1999, pp. 1150-1157. (Pubitemid 32079997)
35. S. Lazebnik, C. Schmid, and J. Ponce, "A sparse texture representation using affine-invariant regions," in Proc. IEEE Comput. Vis. Pattern Recognit., vol. 2. Jun. 2003, pp. 319-324.
36. S. Lazebnik, C. Schmid, and J. Ponce, "Semi-local affine parts for object recognition," in Proc. Brit. Mach. Vis. Conf., 2004, pp. 779-788.
37. I. Jolliffe, Principal Component Analysis (Statistics), 2nd ed. New York: Springer-Verlag, 2002.
38. J. E. Fowler, "Compressive-projection principal component analysis," IEEE Trans. Image Process., vol. 18, no. 10, pp. 2230-2242, Oct. 2009.
39. G. R. Doddington, M. A. Przybocki, A. F. Martin, and D. A. Reynolds, "The NIST speaker recognition evaluation-overview, methodology, systems, results, perspective," Speech Commun., vol. 31, nos. 2-3, pp. 225-254, 2000.
40. R. Rigamonti, M. Brown, and V. Lepetit, "Are sparse representations really relevant for image classification," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., Jun. 2011, pp. 1545-1552.
41. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. New York: Springer- Verlag, 2010, sec. 1.2.