Fast sparse representation with prototypes

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 3618-3625

  Huang, Jia Bin ^a   Yang, Ming Hsuan ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX RELAXATION; LARGE-SCALE PROBLEM; MEMORY REQUIREMENTS; MINIMIZATION PROBLEMS; SPARSE CODING; SPARSE REPRESENTATION; SPEED-UPS; STATE-OF-THE-ART ALGORITHMS; TIME AND SPACE;

COMPUTER VISION; OPTIMIZATION; RELAXATION PROCESSES;

ALGORITHMS;

EID: 77955985602     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539919     Document Type: Conference Paper

References (34)

1. A. Agarwal and B. Triggs. Recovering 3d human pose from monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(1):44-58, 2006. 7
2. M. Aharon, M. Elad, and A. Bruckstein. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11):4311, 2006. 1, 3
3. P. N. Belhumeur and D. J. Kriegman. What is the set of images of an object under all possible illumination conditions. International Journal of Computer Vision, 28(3):1-16, 1998. 1
4. S. Boyd and L. Vandenberghe. Convex optimization. Cambridge university press, 2004. 5
5. A. Bruckstein, D. Donoho, and M. Elad. From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review, 51(1):34-81, 2009. 1, 2, 3, 5
6. O. Bryt and M. Elad. Compression of facial images using the K-SVD algorithm. Journal of Visual Communication and Image Representation, 19(4):270-282, 2008. 3
7. 1-magic; Recovery of sparse signals via convex programming. California Institute of Technology, 2005. http://www.acm.caltech.edu/11magic/. 1, 2, 5, 6
8. E. Candès, J. Romberg, and T. Tao. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59:1207-1223, 2006. 1, 2
9. E. Candes and T. Tao. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Transactions on Information Theory, 52(12):5406-5425, 2006. 1, 2, 4
10. S. Chen, S. Billings, and W. Luo. Orthogonal least squares methods and their application to non-linear system identification. International Journal of Control, 50(5):1873-1896, 1989. 1
11. S. Chen, D. Donoho, and M. Saunders. Automatic decomposition by basis pursuit. SIAM Journal of Scientific Computation, 20(1):33-61, 1998. 1, 3
12. 1-norm minimization problems when the solution may be sparse. IEEE Transactions on Information Theory, 54(11):4789-4812, 2008. 1, 5
13. l-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics, 59(6):797-829, 2006. 1, 2, 3
14. M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15(12):3736-3745, 2006. 1, 3
15. K. Engan, S. O. Aase, and J. H. Husoy. Method of optimal directions for frame design. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pages 2443-2446, 1999. 1, 3
16. T. Ezzat, G. Geiger, and T. Poggio. Trainable videorealistic speech animation. In Proceedings of ACM SIGGRAPH, pages 388-398, 2002. 1
17. K. Huang and S. Aviyente. Sparse representation for signal classification. In NIPS, volume 19, pages 609-616, 2006. 3
18. K. Kreutz-Delgado, J. F. Murray, B. D. Rao, K. Engan, T. W. Lee, and T. J. Sejnowski. Dictionary learning algorithms for sparse representation. Neural Computation, 15(2):349-396, 2003. 3
19. H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. pages 801-808, 2006. 6, 7
20. K.-C. Lee, J. Ho, and D. J. Kriegman. Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5):684-698, 2005. 6
21. M. S. Lewicki and T. J. Sejnowski. Learning overcomplete representations. Neural Computation, 12(2):337-365, 2000. 1, 3
22. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Discriminative learned dictionaries for local image analysis. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008. 3
23. J. Mairal, M. Elad, and G. Sapiro. Sparse representation for color image restoration. IEEE Transactions on Image Processing, 17(1):53, 2008. 3
24. J. Mairal, G. Sapiro, and M. Elad. Learning multiscale sparse representations for image and video restoration. SIAM Multiscale Modeling and Simulation, 7(1):214-241, 2008. 3
25. S. G. Mallat and Z. Zhang. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41(12):3397-3415, 1993. 1
26. H. Murase and S. Nayar. Visual learning and recognition of 3-D objects from appearance. International Journal of Computer Vision, 14(1):5-24, 1995. 1, 7
27. B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583):607-609, 1996. 1, 3
28. B. A. Olshausen and D. J. Field. Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37(23):3311-3325, 1997. 3
29. Y. C. Pati, R. Rezaiifar, and P. S. Krishnaprasad. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition. In Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems, and Computers, pages 40-44, 1993. 1
30. D. Pham and S. Venkatesh. Joint learning and dictionary construction for pattern recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008. 3
31. R. Rubinstein, M. Zibulevsky, and M. Elad. Efficient implementation of the k-svd algorithm using batch orthogonal matching pursuit. Technical Report CS-2008-08, Technion, 2008. 5
32. J. A. Tropp and S. J. Wright. Computational methods for sparse solution of linear inverse problems. Technical Report 2009-01, California Institute of Technology, 2009. 1, 3, 5
33. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2):210-227, 2009. 3
34. J. Yang, J. Wright, T. Huang, and Y. Ma. Image super-resolution as sparse representation of raw image patches. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008. 3, 6, 7