Multiple kernel learning for sparse representation-based classification

IEEE Transactions on Image Processing

Volumn 23, Issue 7, 2014, Pages 3013-3024

  Shrivastava, Ashish ^a   Patel, Vishal M ^a   Chellappa, Rama ^b  

^a UNIVERSITY OF MARYLAND   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

kernel sparse representation; multiple kernel learning; object recognition; Sparse representation based classification

Indexed keywords

ALGORITHMS; CODES (SYMBOLS); IMAGE CLASSIFICATION; MIXING; OBJECT RECOGNITION;

HIGH-DIMENSIONAL FEATURE SPACE; IMAGE CLASSIFICATION ALGORITHMS; KERNEL ALIGNMENTS; MIXING COEFFICIENT; MULTIPLE KERNEL LEARNING; NONLINEAR KERNELS; SPARSE REPRESENTATION; STOPPING CRITERIA;

ITERATIVE METHODS;

EID: 84903184768     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2014.2324290     Document Type: Article

References (55)

1. R. Rubinstein, A. M. Bruckstein, and M. Elad, "Dictionaries for sparse representation modeling," Proc. IEEE, vol. 98, no. 6, pp. 1045-1057, Jun. 2010.
2. J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. Huang, and S. Yan, "Sparse representation for computer vision and pattern recognition," Proc. IEEE, vol. 98, no. 6, pp. 1031-1044, Jun. 2010.
3. M. Elad, M. Figueiredo, and Y. Ma, "On the role of sparse and redundant representations in image processing," Proc. IEEE, vol. 98, no. 6, pp. 972-982, Jun. 2010.
4. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. New York, NY, USA: Springer-Verlag, 2010.
5. S. Chen, D. Donoho, and M. Saunders, "Atomic decomposition by basis pursuit," SIAM J. Sci. Comput., vol. 20, no. 1, pp. 33-61, 1998. (Pubitemid 128689501)
6. Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad, "Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition," in Proc. Conf. Rec. 27th Asilomar Conf. Signals, Syst. Comput., 1993, pp. 40-44.
7. J. A. Tropp, "Greed is good: Algorithmic results for sparse approximation," IEEE Trans. Inf. Theory, vol. 50, no. 10, pp. 2231-2242, Oct. 2004.
8. A. M. Bruckstein, D. L. Donoho, and M. Elad, "From sparse solutions of systems of equations to sparse modeling of signals and images," SIAM Rev., vol. 51, no. 1, pp. 34-81, 2009.
9. K. Etemand and R. Chellappa, "Separability-based multiscale basis selection and feature extraction for signal and image classification," IEEE Trans. Image Process., vol. 7, no. 10, pp. 1453-1465, Oct. 1998. (Pubitemid 128754344)
10. J. Wright, A. Y. 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.
11. V. M. Patel and R. Chellappa, "Sparse representations, compressive sensing and dictionaries for pattern recognition," in Proc. ACPR, 2010.
12. V. M. Patel, R. Chellappa, and M. Tistarelli, "Sparse representations and random projections for robust and cancelable biometrics," in Proc. 11th Int. Conf. Control, Autom., Robot. Vis., Guangzhou, China, Dec. 2010, pp. 1-6.
13. J. K. Pillai, V. M. Patel, R. Chellappa, and N. Ratha, "Secure and robust iris recognition using random projections and sparse representations, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 9, pp. 1877-1893, Sep. 2011.
14. L. Zhang et al., "Kernel sparse representation-based classifier," IEEE Trans. Signal Process., vol. 60, no. 4, pp. 1684-1695, Apr. 2012.
15. S. Gao, I. W. Tsang, and L.-T. Chia, "Sparse representation with kernels," IEEE Trans. Image Process., vol. 22, no. 2, pp. 423-434, Feb. 2013.
16. H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, "Kernel dictionary learning," in Proc. IEEE ICASSP, Mar. 2012, pp. 2021-2024.
17. H. Qi and S. Hughes, "Using the kernel trick in compressive sensing: Accurate signal recovery from fewer measurements," in Proc. IEEE ICASSP, May 2011, pp. 3940-3943.
18. M. Gönen and E. Alpaydin, "Multiple kernel learning algorithms," J. Mach. Learn. Res., vol. 12, pp. 2211-2268, Jul. 2011.
19. V. M. Patel, N. M. Nasrabadi, and R. Chellappa, "Sparsity-motivated automatic target recognition," Appl. Opt., vol. 50, no. 10, pp. 1425-1433, Apr. 2011.
20. H. Li, Y. Gao, and J. Sun, "Fast kernel sparse representation," in Proc. Int. Conf. Digit. Image Comput. Techn. Appl., Dec. 2011, pp. 72-77.
21. X.-T. Yuan and S. Yan, "Visual classification with multi-task joint sparse representation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2010, pp. 3493-3500.
22. S. Gao, I. W. Tsang, and L.-T. Chia, "Kernel sparse representation for image classification and face recognition," in Proc. ECCV, vol. 6314. 2010.
23. H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, "Sparse embedding: A framework for sparsity promoting dimensionality reduction," in Proc. 12th ECCV, 2012.
24. A. Shrivastava, H. V. Nguyen, V. M. Patel, and R. Chellappa, "Design of non-linear discriminative dictionaries for image classification," in Proc. ACCV, 2012.
25. B. Scholkopf and A. J. Smola, Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge, MA, USA: MIT Press, 2001.
26. B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, "Least angle regression," Ann. Statist., vol. 32, no. 2, pp. 407-499, 2004. (Pubitemid 41250302)
27. Y. Chen, N. M. Nasrabadi, and T. D. Tran, "Hyperspectral image classification via kernel sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 217-231, Jan. 2013.
28. G. R. G. Lanckriet, N. Cristianini, P. L. Bartlett, L. E. Ghaoui, and M. I. Jordan, "Learning the kernel matrix with semidefinite programming," J. Mach. Learn. Res., vol. 5, pp. 27-72, Jan. 2004.xxx
29. C. A. Micchelli and M. Pontil, "Learning the kernel function via regularization," J. Mach. Learn. Res., vol. 6, pp. 1099-1125, Jul. 2005.
30. C. S. Ong, A. J. Smola, and R. C. Williamson, "Learning the kernel with hyperkernels," J. Mach. Learn. Res., vol. 6, pp. 1043-1071, Dec. 2005.
31. A. Zien and C. S. Ong, "Multiclass multiple kernel learning," in Proc. 24th ICML, 2007, pp. 1191-1198.
32. Z. Xu, R. Jin, H. Yang, I. King, and M. R. Lyu, "Simple and efficient multiple kernel learning by group lasso," in Proc. Int. Conf. Mach. Learn., 2010, pp. 1175-1182.
33. M. Varma and D. Ray, "Learning the discriminative power-invariance trade-off," in Proc. IEEE 11th ICCV, Oct. 2007, pp. 1-8.
34. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman, "Multiple kernels for object detection," in Proc. 12th ICCV, 2009.
35. H. Tanabe, T. B. Ho, C. H. Nguyen, and S. Kawasaki, "Simple but effective methods for combining kernels in computational biology," in Proc. IEEE Int. Conf. Res., Innov. Vis. Future, Jul. 2008, pp. 71-78.
36. N. Cristianini, J. Shawe-Taylor, A. Elisseeff, and J. S. Kandola, "On kernel-target alignment," in Advances in Neural Information Processing Systems. Cambridge, MA, USA: MIT Press, 2001, pp. 367-373.
37. S. Qiu and T. Lane, "A framework for multiple kernel support vector regression and its applications to siRNA efficacy prediction," IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 6, no. 2, pp. 190-199, Apr./Jun. 2009.
38. L. Fei-Fei, R. Fergus, and P. Perona, "Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories," in Proc. CVPRW, 2004.
39. K. Lai, L. Bo, X. Ren, and D. Fox, "Sparse distance learning for object recognition combining RGB and depth information," in Proc. IEEE ICRA, May 2011, pp. 4007-4013.
40. A. Martinez and R. Benavente, "The ar face database," CVC, Univ. Autònoma de Barcelona, Bellaterra, Barcelona, Tech. Rep. 24, Jun. 1998.
41. A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet, "Simple MKL," J. Mach. Learn. Res., vol. 9, pp. 2491-2521, Nov. 2008.
42. Z. Jiang, Z. Lin, and L. S. Davis, "Learning a discriminative dictionary for sparse coding via label consistent K-SVD," in Proc. IEEE Conf. CVPR, Jun. 2011, pp. 1697-1704.
43. H. Zhang, A. C. Berg, M. Maire, and J. Malik, "SVM-KNN: Discriminative nearest neighbor classification for visual category recognition," in Proc. IEEE Conf. CVPR, Jun. 2006, pp. 2126-2136. (Pubitemid 44931577)
44. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in Proc. IEEE Conf. CVPR, Jun. 2006, pp. 2169-2178. (Pubitemid 44931582)
45. G. Griffin, A. Holub, and P. Perona, "Caltech-256 object category dataset," California Inst. Technology, Pasadena, CA, USA, Tech. Rep. 7694, 2007.
46. O. Boiman, E. Shechtman, and M. Irani, "In defense of nearest-neighbor based image classification," in Proc. IEEE Conf. CVPR, Jun. 2008, pp. 1-8.
47. P. Jain, B. Kulis, and K. Grauman, "Fast image search for learned metrics," in Proc. IEEE Conf. CVPR, Jun. 2008, pp. 1-8.
48. D.-S. Pham and S. Venkatesh, "Joint learning and dictionary construction for pattern recognition," in Proc. IEEE Conf. CVPR, Jun. 2008, pp. 1-8.
49. J. C. van Gemert, J.-M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders, "Kernel codebooks for scene categorization," in Proc. ECCV, 2008.
50. 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.
51. 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.
52. M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: Design of dictionaries for sparse representation," in Proc. SPARS, 2005, pp. 9-12.
53. Q. Zhang and B. Li, "Discriminative K-SVD for dictionary learning in face recognition," in Proc. IEEE Conf. CVPR, Jun. 2010, pp. 2691-2698.
54. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, 2004.
55. A. E. Johnson and M. Hebert, "Using spin images for efficient object recognition in cluttered 3D scenes," IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 5, pp. 433-449, May 1999.