Recursive sparse, spatiotemporal coding

ISM 2009 - 11th IEEE International Symposium on Multimedia

Volumn , Issue , 2009, Pages 645-650

  Dean, Thomas ^a   Washington, Rich ^a   Corrado, Greg ^b  

^a GOOGLE INC   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ATTENTIONAL MECHANISM; AUTOENCODERS; BAND PASS; BASIS VECTOR; DE-NOISING; HUMAN ACTIVITIES; LAYER-BY-LAYERS; MOTION FEATURES; NEW APPROACHES; PERIODIC MOTION; RECEPTIVE FIELDS; RECOGNITION METHODS; RESTRICTED BOLTZMANN MACHINE; SPARSE CODES; SPARSE CODING; SPATIO-TEMPORAL CODES; TRAINING SETS; UNSUPERVISED TRAINING;

BANDPASS FILTERS;

MATHEMATICAL OPERATORS;

EID: 77949530212     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISM.2009.28     Document Type: Conference Paper

References (17)

1. B. A. Olshausen and D. J. Field, "Sparse coding with an overcomplete basis set: A strategy employed by V1?" Vision Research, vol. 37, no. 23, pp. 3311-3325, 1997.
2. A. Hyvärinen, J. Hurri, and J. Väyrynen, "Bubbles: a unifying framework for low-level statistical properties of natural image sequences," Journal of the Optical Society of America, vol. 20, no. 7, pp. 1237-1252, 2003.
3. C. Cadieu and B. Olshausen, "Learning transformational invariants from time-varying natural images," in Advances in Neural Information Processing Systems 21, D. Schuurmans and Y. Bengio, Eds. Cambridge, MA: MIT Press, 2008.
4. D. Mumford, "Neuronal architectures for pattern-theoretic problems," in Large Scale Neuronal Theories of the Brain. MIT Press, 1994, pp. 125-152.
5. J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani, "Path-wise coordinate optimization," Annals of Applied Statistics, vol. 1, no. 2, pp. 302-332, 2007.
6. H. Lee, A. Battle, R. Raina, and A. Y. Ng, "Efficient sparse coding algorithms," in Advances in Neural Information Processing Systems 19, B. Schölkopf, J. Platt, and T. Hofmann, Eds. Cambridge, MA: MIT Press, 2007, pp. 801-808.
7. P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie, "Behavior recognition via sparse spatio-temporal features," in Second Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, October 2005, p. 65.
8. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, "Actions as space-time shapes," Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2247-2253, December 2007.
9. C. Schuldt, I. Laptev, and B. Caputo, "Recognizing human actions: A local SVM approach," in Proceedings of the International Conference on Pattern Recognition. IEEE Computer Society, 2004.
10. I. Laptev and T. Lindeberg, "Space-time interest points," in Proceedings of the ninth IEEE International Conference on Computer Vision, vol. 1, 2003, pp. 432-439.
11. B. A. Olshausen and D. J. Field, "Natural image statistics and efficient coding," Computation in Neural Systems, vol. 7, no. 2, pp. 333-339, 1996.
12. J. H. van Hateren and D. L. Ruderman, "Independent component analysis of natural image sequences yields spatiotemporal filters similar to simple cells in primary visual cortex," Proceedings Royal Society London B, vol. 265, pp. 2315-2320, 1998.
13. B. Olshausen, "Learning sparse, overcomplete representations of time-varying natural images," in Proceedings of the IEEE International Conference on Image Processing, vol. 1. IEEE Computer Society, September 2003, pp. 41-44.
14. Z. Wang and B. Li, "Human activity encoding and recognition using low-level visual features," in Proceedings of the 21th International Joint Conference on Artificial Intelligence, 2009.
15. D. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, November 2004.
16. J. Niebles, H. Wang, and L. Fei-Fei, "Unsupervised learning of human action categories using spatial-temporal words," International Journal of Computer Vision, vol. 79, no. 3, pp. 299-318, 2008.
17. G. Hinton and R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, July 2006.