An adaptive dictionary learning approach for modeling dynamical textures

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2014, Pages 3567-3571

  Wei, Xian ^a   Shen, Hao ^a   Kleinsteuber, Martin ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

dictionary learning; Dynamic textures modeling; linear dynamical systems; sparse representation

Indexed keywords

IMAGE CODING; LINEAR CONTROL SYSTEMS; RANDOM PROCESSES; TEXTURES;

ADAPTIVE DICTIONARY LEARNING; DICTIONARY LEARNING; DYNAMIC TEXTURES; LINEAR DYNAMICAL SYSTEMS; SPARSE REPRESENTATION; TEMPORAL CORRELATIONS; VIDEO REPRESENTATIONS; VISION COMMUNITIES;

SIGNAL PROCESSING;

EID: 84905222845     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2014.6854265     Document Type: Conference Paper

References (16)

1. P.-A. Absil, R. Mahony, and R. Sepulchre. Optimization algorithms on matrix manifolds. Princeton University Press, 2009.
2. B. Boots, G. J. Gordon, and S. M. Siddiqi. A constraint generation approach to learning stable linear dynamical systems. In Advances in Neural Information Processing Systems, pages 1329-1336, 2007.
3. A. B. Chan and N. Vasconcelos. Layered dynamic textures. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(10):1862-1879, 2009.
4. P. M. Djuric, J. H. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. F. Bugallo, and J. Miguez. Particle filtering. Signal Processing Magazine, IEEE, 20(5):19-38, 2003.
5. G. Doretto, A. Chiuso, Y. N. Wu, and S. Soatto. Dynamic textures. International Journal of Computer Vision, 51(2):91-109, 2003.
6. M. Elad and M. Aharon. Image denoising via sparse and redundant representations over learned dictionaries. Image Processing, IEEE Transactions on, 15(12):3736-3745, 2006.
7. B. Ghanem and N. Ahuja. Maximum margin distance learning for dynamic texture recognition. In European Conference on Computer Vision, pages 223-236. Springer, 2010.
8. B. Ghanem and N. Ahuja. Sparse coding of linear dynamical systems with an application to dynamic texture recognition. In Pattern Recognition (ICPR), 2010 20th International Conference on, pages 987-990. IEEE, 2010.
9. S. Hawe, M. Seibert, and M. Kleinsteuber. Separable dictionary learning. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 438-445, June 2013.
10. B. K. Horn and B. G. Schunck. Determining optical flow. Artificial intelligence, 17(1):185-203, 1981.
11. V. Kwatra, A. Schödl, I. Essa, G. Turk, and A. Bobick. Graphcut textures: Image and video synthesis using graph cuts. In Graphics (TOG), ACM Transactions on, volume 22, pages 277-286. ACM, 2003.
12. P. Saisan, G. Doretto, Y. N. Wu, and S. Soatto. Dynamic texture recognition. In Computer Vision and Pattern Recognition. IEEE Computer Society Conference on, volume 2, pages II-58. IEEE, 2001.
13. A. Schödl, R. Szeliski, D. H. Salesin, and I. Essa. Video textures. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 489-498. ACM Press/Addison-Wesley Publishing Co., 2000.
14. M. Szummer and R. W. Picard. Temporal texture modeling. In International Conference on Image Processing., volume 3, pages 823-826. IEEE, 1996.
15. J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(2):210-227, 2009.
16. H. Zou and T. Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301-320, 2005.