Manifold regularized multitask learning for semi-supervised multilabel image classification

IEEE Transactions on Image Processing

Volumn 22, Issue 2, 2013, Pages 523-536

  Luo, Yong ^a   Tao, Dacheng ^b   Geng, Bo ^a   Xu, Chao ^a   Maybank, Stephen J ^c  

^a Peking University   (China)

^b UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^c UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Image classification; manifold; multilabel; semi supervised; shared subspace

Indexed keywords

BINARY CLASSIFIERS; CLASSIFICATION PERFORMANCE; COMMON STRUCTURES; DATA DISTRIBUTION; DATA MANIFOLDS; DATA SETS; GEOMETRIC STRUCTURE; HIGH-DIMENSIONAL; HYPOTHESIS SPACE; IMAGE CLASSIFICATION ALGORITHMS; MANIFOLD; MODEL COMPLEXITY; MULTI-LABEL; MULTIPLE CLASSIFICATION; MULTIPLE LABELS; MULTITASK LEARNING; SEARCH VOLUME; SEMI-SUPERVISED; SHARED SUBSPACE; VISUAL FEATURE;

ALGORITHMS;

IMAGE CLASSIFICATION;

EID: 84872258670     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2218825     Document Type: Article

References (45)

1. B. Geng, L. Yang, C. Xu, and X. Hua, "Content-aware ranking for visual search," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 3400-3407.
2. N. Vaswani and R. Chellappa, "Principal components null space analysis for image and video classification," IEEE Trans. Image Process., vol. 15, no. 7, pp. 1816-1830, Jul. 2006. (Pubitemid 43985063)
3. J. Fowler, "Compressive-projection principal component analysis," IEEE Trans. Image Process., vol. 18, no. 10, pp. 2230-2242, Oct. 2009.
4. M. Seeger, "Learning with labeled and unlabeled data," Inst. Adaptive and Neural Computation, Univ. Edinburgh, Edinburgh, U.K., Tech. Rep. TR.2001, 2001.
5. O. Chapelle, B. Schölkopf, and A. Zien, Semi-Supervised Learning. Cambridge, MA: MIT Press, 2006.
6. X. Zhu, "Semi-supervised learning literature survey," Dept. Electr. Eng., Univ. Wisconsin-Madison, Madison, Tech. Rep. 1530, 2006.
7. M. Belkin, P. Niyogi, and V. Sindhwani, "Manifold regularization: A geometric framework for learning from labeled and unlabeled examples," J. Mach. Learn. Res., vol. 7, pp. 2399-2434, Aug. 2006. (Pubitemid 44708005)
8. B. Geng, D. Tao, C. Xu, L. Yang, and X. Hua, "Ensemble manifold regularization," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 6, pp. 1227-1233, Jun. 2012.
9. X. He, W. Ma, and H. Zhang, "Learning an image manifold for retrieval," in Proc. 12th Annu. ACM Int. Conf. Multimedia, 2004, pp. 17-23. (Pubitemid 40211713)
10. M. Davenport, C. Hegde, M. Duarte, and R. Baraniuk, "Joint manifolds for data fusion," IEEE Trans. Image Process., vol. 19, no. 10, pp. 2580-2594, Oct. 2010.
11. E. Kokiopoulou, D. Kressner, and P. Frossard, "Optimal image alignment with random projections of manifolds: Algorithm and geometric analysis," IEEE Trans. Image Process., vol. 20, no. 6, pp. 1543-1557, Jun. 2011.
12. S. Si, D. Tao, and B. Geng, "Bregman divergence-based regularization for transfer subspace learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 7, pp. 929-942, Jul. 2010.
13. X. Tian, D. Tao, and Y. Rui, "Sparse transfer learning for interactive video search reranking," ACM Trans. Multimedia Comput., Commun., Appl., vol. 8, no. 3, pp. 1-17, 2012.
14. B. Geng, L. Yang, C. Xu, and X. Hua, "Ranking model adaptation for domain-specific search," in Proc. 18th ACM Conf. Inf. Knowl. Manag., 2009, pp. 197-206.
15. R. Caruana, "Multitask learning," Mach. Learn., vol. 28, no. 1, pp. 41-75, 1997. (Pubitemid 127507169)
16. R. Ando and T. Zhang, "A framework for learning predictive structures from multiple tasks and unlabeled data," J. Mach. Learn. Res., vol. 6, pp. 1817-1853, Nov. 2005.
17. S. Ji, L. Tang, S. Yu, and J. Ye, "A shared-subspace learning framework for multi-label classification," ACM Trans. Knowl. Disc. Data, vol. 4, no. 2, pp. 1-29, 2010.
18. A. Torralba, K. Murphy, and W. Freeman, "Sharing visual features for multiclass and multiview object detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 5, pp. 854-869, May 2007. (Pubitemid 46534781)
19. D. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
20. A. Oliva and A. Torralba, "Modeling the shape of the scene: A holistic representation of the spatial envelope," Int. J. Comput. Vis., vol. 42, no. 3, pp. 145-175, 2001. (Pubitemid 32680801)
21. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zis-serman. (2007). The PASCAL Visual Object Classes Challenge Results [Online]. Available: http://pascallin.ecs.soton.ac.uk/challenges/VOC/voc2012/index.html
22. M. J. Huiskes and M. S. Lew, "The MIR flickr retrieval evaluation," in Proc. 1st ACM Int. Conf. Multimedia Inf. Retr., 2008, pp. 39-43.
23. A. Blum and T. Mitchell, "Combining labeled and unlabeled data with co-training," in Proc. 11th Annu. Conf. Comput. Learn. Theory, 1998, pp. 92-100.
24. M. Guillaumin, J. Verbeek, and C. Schmid, "Multimodal semi-supervised learning for image classification," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 902-909.
25. K. Nigam, A. McCallum, S. Thrun, and T. Mitchell, "Text classification from labeled and unlabeled documents using EM," Mach. Learn., vol. 39, no. 2, pp. 103-134, 2000. (Pubitemid 30594822)
26. O. Chapelle and A. Zien, "Semi-supervised classification by low density separation," in Proc. 10th Int. Workshop Artif. Intell. Stat., 2005, pp. 57-64.
27. T. Joachims, "Transductive inference for text classification using support vector machines," in Proc. 16th Int. Conf. Mach. Learn., 1999, pp. 200-209.
28. M. Szummer and T. Jaakkola, "Partially labeled classification with Markov random walks," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2002, pp. 945-952.
29. 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.
30. M. Boutell, J. Luo, X. Shen, and C. Brown, "Learning multilabel scene classification," Pattern Recognit., vol. 37, no. 9, pp. 1757-1771, 2004.
31. G. Tsoumakas and I. Katakis, "Multilabel classification: An overview," Int. J. Data Warehous. Min., vol. 3, no. 3, pp. 1-13, 2007.
32. A. McCallum, "Multilabel text classification with a mixture model trained by EM," in Proc. AAAI Workshop Text Learn., 1999, pp. 1-6.
33. R. Schapire and Y. Singer, "Boostexter: A boosting-based system for text categorization," Mach. Learn., vol. 39, no. 2, pp. 135-168, 2000. (Pubitemid 30594821)
34. J. Baxter, "A model of inductive bias learning," J. Artif. Intell. Res., vol. 12, pp. 149-198, Jun. 2000.
35. S. Thrun and J. O'Sullivan, "Discovering structure in multiple learning tasks: The TC algorithm," in Proc. Int. Conf. Mach. Learn., 1996, pp. 489-497.
36. L. Jacob, F. Bach, and J. Vert, "Clustered multitask learning: A convex formulation," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2008, pp. 745-752.
37. S. Bickel, J. Bogojeska, T. Lengauer, and T. Scheffer, "Multitask learning for HIV therapy screening," in Proc. 25th Int. Conf. Mach. Learn., 2008, pp. 56-63.
38. A. Argyriou, T. Evgeniou, and M. Pontil, "Convex multitask feature learning," Mach. Learn., vol. 73, no. 3, pp. 243-272, 2008.
39. T. Evgeniou, C. Micchelli, and M. Pontil, "Learning multiple tasks with kernel methods," J. Mach. Learn. Res., vol. 6, no. 1, pp. 615-637, 2005.
40. Y. Amit, M. Fink, N. Srebro, and S. Ullman, "Uncovering shared structures in multiclass classification," in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 17-24.
41. J. Leiva-Murillo, L. Gómez-Chova, and G. Camps-Valls, "Multitask remote sensing data classification," IEEE Trans. Geosci. Remote Sens., vol. PP, no. 99, pp. 1-11, Jan. 2012.
42. A. Quattoni, M. Collins, and T. Darrell, "Learning visual representations using images with captions," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Feb. 2007, pp. 1-8.
43. B. Schölkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA: MIT Press, 2002.
44. N. Guan, D. Tao, Z. Luo, and J. Shawe-Taylor, "MahNMF: Manhattan non-negative matrix factorization," Mach. Learn., vol. 1, no. 5, pp. 1-43, 2012.
45. M. Zhu, "Recall, precision and average precision," Dept. Electr. Eng., Univ. Waterloo, Waterloo, ON, Canada, Tech. Rep., Working Paper 2004-09, 2004.