Sparse label-indicator optimization methods for image classification

IEEE Transactions on Image Processing

Volumn 23, Issue 3, 2014, Pages 1002-1014

  Jing, Liping ^a   Ng, Michael K ^b  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b HONG KONG BAPTIST UNIVERSITY   (Hong Kong)

Author keywords

Graph; image classification; multi class; random walk with restart; semi supervised learning; sparsity

Indexed keywords

GRAPH; MULTI-CLASS; RANDOM WALK WITH RESTART; SEMI-SUPERVISED LEARNING; SPARSITY;

CONVEX OPTIMIZATION; SUPERVISED LEARNING; TESTING;

IMAGE CLASSIFICATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84893836262     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2013.2294546     Document Type: Article

References (57)

1. D. W. Aha, "Lazy learning: Special issue editorial," Artif. Intell. Rev., vol. 11, nos. 1-5, pp. 7-10, 1997.
2. D. Bertsekas, Nonlinear Programming. Belmont, MA, USA: Athena Scientific, 1999.
3. A. Blum and S. Chawla, "Learning from labeled and unlabeled data using graph mincuts," in Proc. 18th ICML, 2001, pp. 19-26.
4. A. Bosch, A. Zisserman, and X. Munoz, "Scene classification via pLSA," in Proc. ECCV, 2005, pp. 517-530. (Pubitemid 44029655)
5. A. Bosch, A. Zisserman, and X. Muñoz, "Image classification using random forests and ferns," in Proc. IEEE 11th ICCV, Oct. 2007, pp. 1-8.
6. Y. Boureau, F. Bach, Y. LeCun, and J. Ponce, "Learning midlevel features for recognition," in Proc. IEEE CVPR, Jun. 2010, pp. 2559-2566.
7. M. Boutell, J. Luo, X. Shen, and C. Brown, "Learning multi-label scene classification," Pattern Recognit., vol. 37, no. 9, pp. 1757-1771, 2004. (Pubitemid 38804465)
8. L. Bruzzone, M. Chi, and M. Marconcini, "A novel transductive SVM for semisupervised classification of remote-sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3363-3373, Nov. 2006.
9. M. Carreira and R. Zemel, "Proximity graphs for clustering and manifold learning," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2005.
10. L. Chova, G. Valls, J. Mari, and J. Calpe, "Semisupervised image classification with Laplacian support vector machines," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3, pp. 336-340, Jul. 2008.
11. F. Chung, Spectral Graph Theory. Providence, RI, USA: AMS, 1997.
12. C. Cortes and V. Vapnik, "Support vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
13. N. Craswell and M. Szummer, "Random walks on the click graph," in Proc. 30th Annu. Int. ACM SIGIR, 2007, pp. 239-246. (Pubitemid 350164967)
14. M. Culp and G. Michailidis, "Graph-based semi-supervised learning," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 1, pp. 174-179, Jan. 2008.
15. D. Donoho, "For most large underdetermined systems of linear equations the minimal-1-norm solution is also the sparsest solution," Commun. Pure Appl. Math., vol. 59, no. 6, pp. 797-829, 2006.
16. A. Elisseeff and J. Weston, "A kernel method for multi-labelled classification," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2002, pp. 681-687.
17. E. Elhamifar and R. Vidal, "Sparse subspace clustering," in Proc. IEEE Conf. CVPR, Jun. 2009, pp. 2790-2797.
18. E. Elhamifar and R. Vidal, "Sparse subspace clustering: Algorithm, theory, and applications," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 11, pp. 2765-2781, Nov. 2013.
19. L. Fei-Fei and P. Perona, "A Bayesian hierarchical model for learning natural scene categories," in Proc. IEEE Comput. Soc. Conf. CVPR, Jun. 2005, pp. 524-531.
20. J. He, M. Li, H. Zhang, H. Tong, and C. Zhang, "Manifold-ranking based image retrieval," in Proc. 12th Annu. ACM Int. Conf. Multimedia, 2004, pp. 9-16. (Pubitemid 40211712)
21. T. Jebara, J. Wang, and S. Chang, "Graph construction and b-matching for semi-supervised learning," in Proc. 26th Annu. ICML, 2009, pp. 441-448.
22. L. Jing, C. Zhang, and M. Ng, "SNMFCA: Supervised NMF-based image classification and annotation," IEEE Trans. Image Process., vol. 21, no. 11, pp. 4508-4521, Nov. 2012.
23. J. Kang and L. Guo, "A semi-supervised approach to image classification in an urban environment," in Proc. CVPR, 2008, pp. 1-8.
24. S. Karayev, M. Fritz, S. Fidler, and T. Darrell, "A probabilistic model for recursive factorized image features," in Proc. IEEE Conf. CVPR, Jun. 2011, pp. 401-408.
25. I. Konstas, V. Stathopoulos, and J. Jose, "On social networks and collaborative recommendation," in Proc. 32nd Int. ACM SIGIR, 2009, pp. 195-202.
26. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in Proc. IEEE Comput. Soc. Conf. CVPR, Jun. 2006, pp. 2169-2178. (Pubitemid 44931582)
27. L. Li and L. Fei-Fei, "What, where and who? Classifying events by scene and object recognition," in Proc. IEEE 11th ICCV, Oct. 2007, pp. 1-8.
28. C. Lin, "Projected gradient methods for nonnegative matrix factorization," Neural Comput., vol. 19, no. 10, pp. 93-116, 2007.
29. C. Lin and J. More, "Newton's method for large-scale bound constrained problems," SIAM J. Optim., vol. 9, no. 4, pp. 1100-1127, 1999. (Pubitemid 129489319)
30. L. Lovasz, "Random walks on graphs: A survey," Combinatronics, vol. 2, no. 1, pp. 1-46, 1996.
31. U. von Luxburg, "A tutorial on spectral clustering," J. Statist. Comput., vol. 17, no. 4, pp. 395-416, 2007.
32. X. Ma, Y. Tan, Y. Zhao, and H. Tian, "Face recognition based on maximizing margin and discriminant locality preserving projection," J. Inf. Comput. Sci., vol. 7, no. 7, pp. 1551-1559, 2010.
33. O. Maron and A. Ratan, "Multiple-instance learning for natural scene classification," in Proc. 15th ICML, 1998, pp. 341-349.
34. S. Melacci and M. Belkin, "Laplacian support vector machines trained in the primal," J. Mach. Learn. Res., vol. 12, no. 3, pp. 1149-1184, 2011.
35. L. Page, S. Brin, R. Motwani, and T. Winograd, "The pagerank citation ranking: Bringing order to the web," Stanford Digital Library Technologies Project, Tech. Rep. SIDL-WP-1999-0120, Stanford InfoLab., 1999.
36. 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, nos. 1-3, pp. 157-173, 2008.
37. 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)
38. M. Szummer and T. Jaakkola, "Partially labeled classification with Markov random walks," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2003.
39. H. Tong and C. Faloutsos, "Center-piece subgraphs: Problem definition and fast solutions," in Proc. 12th ACM SIGKDD, 2006, pp. 404-413. (Pubitemid 44535537)
40. H. Tong, C. Faloutsos, and J. Pan, "Fast random walk with restart and its applications," in Proc. 6th ICDM, 2006, pp. 613-622.
41. G. Valls, T. Bandos, and D. Zhou, "Semi-supervised graph-based hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3044-3054, Oct. 2008.
42. C. Wang, D. Blei, and F. Li, "Simultaneous image classification and annotation," in Proc. IEEE Conf. CVPR, Jun. 2009, pp. 1903-1910.
43. C. Wang, F. Jing, L. Zhang, and H. Zhang, "Image annotation refinement using random walk with restarts," in Proc. 14th Annu. ACM Int. Conf. Multimedia, 2006, pp. 647-650. (Pubitemid 47113598)
44. F. Wang and C. Zhang, "Label propagation through linear neighborhoods," in Proc. ICML, 2006, pp. 985-992. (Pubitemid 44483031)
45. H. Wang, H. Huang, and C. Ding, "Image annotation using birelational graph of images and semantic labels," in Proc. CVPR, 2012, pp. 793-800.
46. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, "Localityconstrained linear coding for image classification," in Proc. IEEE Conf. CVPR, Jun. 2010, pp. 3360-3367.
47. Q. Wu, M. Ng, and Y. Ye, "Markov-MIML: A Markov chain based multi-instance multi-label learning algorithm," Knowl. Inf. Syst., vol. 37, no. 1, pp. 83-104, 2013.
48. Y. Xu, X. Yi, and C. Zhang, "A random walks method for text classification," in Proc. SIAM DM, 2006, pp. 1-8.
49. J. Yang, K. Yu, Y. Gong, and T. Huang, "Linear spatial pyramid matching using sparse coding for image classification," in Proc. IEEE Conf. CVPR, Jun. 2009, pp. 1794-1801.
50. L. Zelnik and P. Perona, "Self-tuning spectral clustering," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2004.
51. C. Zhang, H. Shen, J. Wang, F. Wang, and L. Quan, "Linear neighborhood propagation and its applications," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 9, pp. 1600-1615, Sep. 2009.
52. M. Zhang and Z. Zhou, "ML-KNN: A lazy learning approach to multilabel learning," Pattern Recognit., vol. 40, no. 7, pp. 2038-2048, 2007. (Pubitemid 46497248)
53. M. Zhang and Z. Zhou, "M3MIML: A maximum margin method for multi-instance multi-label learning," in Proc. 8th IEEE ICDM, Dec. 2008, pp. 688-697.
54. X. Zhang and W. Lee, "Hyperparameter learning for graph based semisupervised learning," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2007, pp. 1585-1592.
55. D. Zhou, O. Bousquet, T. Lal, J. Weston, and B. Scholkopf, "Learning with local and global consistency," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2004.
56. Z. Zhou and M. Zhang, "Multi-instance multi-label learning with application to scene classification," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2007.
57. X. Zhu, "Semi-supervised learning literature survey," Univ. Wisconsin, Madison, WI, USA, Dept. Comput. Sci., Tech. Rep. 15304, 2006.