Table of Contents Author Guidelines Submit a Manuscript
Table of Contents Alerts

To receive news and publication updates for Mathematical Problems in Engineering, enter your email address in the box below.

Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 3824027, 15 pages
http://dx.doi.org/10.1155/2016/3824027
Research Article

A Weighted Block Dictionary Learning Algorithm for Classification

Zhongrong Shi

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China

Received 3 April 2016; Accepted 30 June 2016

Academic Editor: Yaguo Lei

Copyright © 2016 Zhongrong Shi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Mairal, F. Bach, and J. Ponce, “Task-driven dictionary learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 4, pp. 791–804, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311–4322, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. I. Ramirez, P. Sprechmann, and G. Sapiro, “Classification and clustering via dictionary learning with structured incoherence and shared features,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 3501–3508, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Yang, L. Zhang, X. Feng, and D. Zhang, “Sparse representation based Fisher discrimination dictionary learning for image classification,” International Journal of Computer Vision, vol. 109, no. 3, pp. 209–232, 2014. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  6. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, “Discriminative learned dictionaries for local image analysis,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, Anchorage, Alaska, USA, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Castrodad and G. Sapiro, “Sparse modeling of human actions from motion imagery,” International Journal of Computer Vision, vol. 100, no. 1, pp. 1–15, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. G. Shao, Y. Wu, A. Yong, X. Liu, and T. Guo, “Fingerprint compression based on sparse representation,” IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 489–501, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. O. G. Sezer, O. G. Guleryuz, and Y. Altunbasak, “Approximation and compression with sparse orthonormal transforms,” IEEE Transactions on Image Processing, vol. 24, no. 8, pp. 2328–2343, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. J. Zhang, D. Zhao, and W. Gao, “Group-based sparse representation for image restoration,” IEEE Transactions on Image Processing, vol. 23, no. 8, pp. 3336–3351, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. W. Dong, L. Zhang, G. Shi, and X. Li, “Nonlocally centralized sparse representation for image restoration,” IEEE Transactions on Image Processing, vol. 22, no. 4, pp. 1620–1630, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. W. Dong, L. Zhang, G. Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Transactions on Image Processing, vol. 20, no. 7, pp. 1838–1857, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. M. Carlavan and L. Blanc-Féraud, “Sparse Poisson noisy image deblurring,” IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1834–1846, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  14. K. I. Kim and Y. Kwon, “Single-image super-resolution using sparse regression and natural image prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127–1133, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Transactions on Image Processing, vol. 19, no. 11, pp. 2861–2873, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  16. B. Liu, J. Huang, C. Kulikowski, and L. Yang, “Robust visual tracking using local sparse appearance model and k-selection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 12, pp. 2968–2981, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Hu, W. Li, X. Zhang, and S. Maybank, “Single and multiple object tracking using a multi-feature joint sparse representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 4, pp. 816–833, 2015. View at Publisher · View at Google Scholar · View at Scopus
  18. A. M. Martinez and R. Benavente, “The AR face database,” CVC Technical Report 24, The Ohio State University, 1998. View at Google Scholar
  19. J. Yang, K. Yu, and T. Huang, “Supervised translation-invariant sparse coding,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 3517–3524, IEEE, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. Q. Zhang and B. Li, “Discriminative K-SVD for dictionary learning in face recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 2691–2698, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Bengio, F. Pereira, Y. Singer, and D. Strelow, “Group sparse coding,” in Proceedings of the 23rd Annual Conference on Neural Information Processing Systems (NIPS '09), pp. 82–89, December 2009. View at Scopus
  22. J. Mairal, J. Ponce, G. Sapiro, A. Zisserman, and F. R. Bach, “Supervised dictionary learning,” in Advances in Neural Information Processing Systems, pp. 1033–1040, 2009. View at Google Scholar
  23. Z. Jiang, Z. Lin, and L. S. Davis, “Label consistent K-SVD: learning a discriminative dictionary for recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2651–2664, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Zhang, Z. Jiang, and L. S. Davis, “Learning structured low-rank representations for image classification,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 676–683, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Kong and D. Wang, “A dictionary learning approach for classification: separating the particularity and the commonality,” in Computer Vision—ECCV 2012, pp. 186–199, Springer, 2012. View at Google Scholar
  26. N. Zhou, Y. Shen, J. Peng, and J. Fan, “Learning inter-related visual dictionary for object recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 3490–3497, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. L. Shen, S. Wang, G. Sun, S. Jiang, and Q. Huang, “Multi-level discriminative dictionary learning towards hierarchical visual categorization,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 383–390, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Yang, D. Dai, L. Shen, and L. Van Gool, “Latent dictionary learning for sparse representation based classification,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 4138–4145, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Cai, W. Zuo, L. Zhang, X. Feng, and P. Wang, “Support vector guided dictionary learning,” in Computer Vision—ECCV 2014, pp. 624–639, Springer, 2014. View at Google Scholar
  30. Y. C. Pati, R. Rezaiifar, and P. Krishnaprasad, “Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition,” in Proceedings of the Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, pp. 40–44, 1993.
  31. R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society. Series B. Methodological, vol. 58, no. 1, pp. 267–288, 1996. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  32. E. Elhamifar and R. Vidal, “Robust classification using structured sparse representation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 1873–1879, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. Y.-T. Chi, M. Ali, A. Rajwade, and J. Ho, “Block and group regularized sparse modeling for dictionary learning,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 377–382, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. H. Lee, A. Battle, R. Raina, and A. Y. Ng, “Efficient sparse coding algorithms,” in Proceedings of the 20th Annual Conference on Neural Information Processing Systems (NIPS '06), pp. 801–808, December 2006. View at Scopus
  35. A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: illumination cone models for face recognition under variable lighting and pose,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643–660, 2001. View at Publisher · View at Google Scholar · View at Scopus
  36. 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,” Computer Vision and Image Understanding, vol. 106, no. 1, pp. 59–70, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. D. G. Lowe, “Object recognition from local scale-invariant features,” in Proceedings of the 7th IEEE International Conference on Computer Vision (ICCV '99), pp. 1150–1157, Kerkyra, Greece, September 1999. View at Scopus
  38. J. Yang, K. Yu, Y. Gong, and T. Huang, “Linear spatial pyramid matching using sparse coding for image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '09), pp. 1794–1801, 2009.