Learning convolutional feature hierarchies for visual recognition

Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Volumn , Issue , 2010, Pages

  Kavukcuoglu, Koray ^a   Sermanet, Pierre ^a   Boureau, Y Lan ^a,b   Gregor, Karol ^a   Mathieu, Michaël ^a   LeCun, Yann ^a  

^a NEW YORK UNIVERSITY   (United States)

^b INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; CONVOLUTION; EDGE DETECTION; IMAGE ENHANCEMENT;

FEATURE HIERARCHIES; FEATURES VECTOR; IMAGE WINDOW; LARGE IMAGES; MULTI-STAGES; REDUNDANT CODES; SPARSE CODING; UNSUPERVISED METHOD; VISUAL FEATURE; VISUAL RECOGNITION;

NETWORK ARCHITECTURE;

EID: 85162460675     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. LeCun, Y, Bottou, L, Bengio, Y, and Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, November 1998.
2. Serre, T, Wolf, L, and Poggio, T. Object recognition with features inspired by visual cortex. In CVPR'05 - Volume 2, pages 994-1000, Washington, DC, USA, 2005. IEEE Computer Society.
3. Lee, H, Grosse, R, Ranganath, R, and Ng, A. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In ICML'09, pages 609-616. ACM, 2009.
4. Ranzato, M, Poultney, C, Chopra, S, and LeCun, Y. Efficient learning of sparse representations with an energy-based model. In NIPS'07. MIT Press, 2007.
5. Kavukcuoglu, K, Ranzato, M, Fergus, R, and LeCun, Y. Learning invariant features through topographic filter maps. In CVPR'09. IEEE, 2009.
6. Zeiler, M, Krishnan, D, Taylor, G, and Fergus, R. Deconvolutional Networks. In CVPR'10. IEEE, 2010.
7. Aharon, M, Elad, M, and Bruckstein, A. M. K-SVD and its non-negative variant for dictionary design. In Papadakis, M, Laine, A. F, and Unser, M. A, editors, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, volume 5914, pages 327-339, August 2005.
8. Mairal, J, Bach, F, Ponce, J, and Sapiro, G. Online dictionary learning for sparse coding. In ICML'09, pages 689-696. ACM, 2009.
9. Li, Y and Osher, S. Coordinate Descent Optimization for l1 Minimization with Application to Compressed Sensing; a Greedy Algorithm. CAM Report, pages 09-17.
10. Olshausen, B. A and Field, D. J. Sparse coding with an overcomplete basis set: a strategy employed by v1? Vision Research, 37(23):3311-3325, 1997.
11. Beck, A and Teboulle, M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Img. Sci., 2(1):183-202, 2009.
12. Mallat, S and Zhang, Z. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41(12):3397:3415, 1993.
13. Martin, D, Fowlkes, C, Tal, D, andMalik, J. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV'01, volume 2, pages 416-423, July 2001.
14. Jarrett, K, Kavukcuoglu, K, Ranzato, M, and LeCun, Y. What is the best multi-stage architecture for object recognition? In ICCV'09. IEEE, 2009.
15. Gregor, K and LeCun, Y. Learning fast approximations of sparse coding. In Proc. International Conference on Machine learning (ICML'10), 2010.
16. LeCun, Y, Bottou, L, Orr, G, and Muller, K. Efficient backprop. In Orr, G and K., M, editors, Neural Networks: Tricks of the trade. Springer, 1998.
17. Schwartz, O and Simoncelli, E. P. Natural signal statistics and sensory gain control. Nature Neuroscience, 4(8):819-825, August 2001.
18. Lyu, S and Simoncelli, E. P. Nonlinear image representation using divisive normalization. In CVPR'08. IEEE Computer Society, Jun 23-28 2008.
19. Fei-Fei, L, Fergus, R, and Perona, P. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In Workshop on Generative-Model Based Vision, 2004.
20. Pinto, N, Cox, D. D, and DiCarlo, J. J. Why is real-world visual object recognition hard? PLoS Comput Biol, 4(1):e27, 01 2008.
21. Lazebnik, S, Schmid, C, and Ponce, J. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. CVPR'06, 2:2169-2178, 2006.
22. Boureau, Y, Bach, F, LeCun, Y, and Ponce, J. Learning mid-level features for recognition. In CVPR'10. IEEE, 2010.
23. Dalal, N and Triggs, B. Histograms of oriented gradients for human detection. In Schmid, C, Soatto, S, and Tomasi, C, editors, CVPR'05, volume 2, pages 886-893, June 2005.
24. Walk, S, Majer, N, Schindler, K, and Schiele, B. New features and insights for pedestrian detection. In CVPR 2010, San Francisco, California.
25. Dollár, P, Wojek, C, Schiele, B, and Perona, P. Pedestrian detection: A benchmark. In CVPR'09. IEEE, June 2009.
26. Dollár, P, Tu, Z, Perona, P, and Belongie, S. Integral channel features. In BMVC 2009, London, England.
27. Dollár, P, Belongie, S, and Perona, P. The fastest pedestrian detector in the west. In BMVC 2010, Aberystwyth, UK.
28. Felzenszwalb, P, Girshick, R, McAllester, D, and Ramanan, D. Object detection with discriminatively trained part based models. In PAMI 2010.