Deep roto-translation scattering for object classification

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

Volumn 07-12-June-2015, Issue , 2015, Pages 2865-2873

  Oyallon, Edouard ^a   Mallat, Stéphane ^a  

^a ECOLE NORMALE SUPÉRIEURE   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPLEX NETWORKS; COMPUTER VISION; CONVOLUTION; LEARNING ALGORITHMS; PATTERN RECOGNITION;

ANGULAR VARIABLES; COMPLEX WAVELETS; DEEP LEARNING; DICTIONARY LEARNING ALGORITHMS; FEATURE REPRESENTATION; IMAGE DATABASE; IMAGE REPRESENTATIONS; OBJECT CLASSIFICATION;

IMAGE CLASSIFICATION;

EID: 84959232418     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298904     Document Type: Conference Paper

References (30)

1. T. Blumensath and M. E. Davies. On the difference between orthogonal matching pursuit and orthogonal least squares. 2007
2. L. Bo, X. Ren, and D. Fox. Multipath Sparse Coding Using Hierarchical Matching Pursuit. In CVPR, pages 660-667. IEEE, 2013
3. Y.-L. Boureau, N. L. Roux, F. Bach, J. Ponce, and Y. LeCun. Ask the locals: Multi-way local pooling for image recognition. In D. N. Metaxas, L. Quan, A. Sanfeliu, and L. J. V. Gool, editors, ICCV, pages 2651-2658. IEEE, 2011
4. J. Bruna. Signal and Texture Recovery from Scattering Representations. 2014
5. J. Bruna and S. Mallat. Invariant Scattering Convolution Networks. IEEE Trans. Pattern Anal. Mach. Intell., 35(8):1872-1886, 2013
6. S. Chen, C. F. Cowan, and P. M. Grant. Orthogonal least squares learning algorithm for radial basis function networks. Neural Networks, IEEE Transactions on, 2(2):302-309, 1991
7. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886-893. IEEE, 2005
8. R. B. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR, abs/1311. 2524, 2013
9. K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. In Computer Vision-ECCV 2014, pages 346-361. Springer, 2014
10. Y. Jia, C. Huang, and T. Darrell. Beyond spatial pyramids: Receptive field learning for pooled image features. In CVPR, pages 3370-3377. IEEE, 2012
11. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. In P. L. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, editors, NIPS, pages 1106-1114, 2012
12. S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In CVPR (2), pages 2169-2178. IEEE Computer Society, 2006
13. Q. V. Le, M. Ranzato, R. Monga, M. Devin, G. Corrado, K. Chen, J. Dean, and A. Y. Ng. Building high-level features using large scale unsupervised learning. In ICML. icml. cc /Omnipress, 2012
14. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Back-Propagation Applied to Handwritten Zip Code Recognition. Neural Computation, 1(4):541-551, 1989
15. Y. LeCun, K. Kavukcuoglu, and C. Farabet. Convolutional networks and applications in vision. In ISCAS, pages 253-256. IEEE, 2010
16. C.-Y. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu. Deeply-Supervised Nets. arXiv preprint arXiv:1409. 5185, 2014
17. H. Lee, R. B. Grosse, R. Ranganath, and A. Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In A. P. Danyluk, L. Bottou, and M. L. Littman, editors, ICML, volume 382 of ACM International Conference Proceeding Series, page 77. ACM, 2009
18. T.-H. Lin and H. EDU. Stable and Efficient Representation Learning with Nonnegativity Constraints
19. D. Lowe. Distinctive image features from scale-invariant key-points. Intl. Journal of Computer Vision, 60:91-110, 2004
20. M. Malinowski and M. Fritz. Learning smooth pooling regions for visual recognition. In the British Machine Vision Conference, 2013
21. S. Mallat. A Wavelet Tour of Signal Processing. San Diego: Academic Press, 1999
22. S. Mallat. Group Invariant Scattering. CoRR, abs/1101. 2286, 2011
23. S. McCann and D. G. Lowe. Spatially Local Coding for Object Recognition. In K. M. Lee, Y. Matsushita, J. M. Rehg, and Z. Hu, editors, ACCV (1), volume 7724 of Lecture Notes in Computer Science, pages 204-217. Springer, 2012
24. A. M. Saxe, P. W. Koh, Z. Chen, M. Bhand, B. Suresh, and A. Y. Ng. On Random Weights and Unsupervised Feature Learning. In L. Getoor and T. Scheffer, editors, ICML, pages 1089-1096. Omnipress, 2011
25. L. Sifre and S. Mallat. Rotation, Scaling and Deformation Invariant Scattering for Texture Discrimination. In CVPR, pages 1233-1240. IEEE, 2013
26. J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman. Discovering objects and their location in images. volume 1, pages 370-377, 2005
27. K. Sohn and H. Lee. Learning Invariant Representations with Local Transformations. In ICML. icml. cc /Omnipress, 2012
28. C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus. Intriguing properties of neural networks. CoRR, abs/1312. 6199, 2013
29. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In W. W. Cohen, A. McCallum, and S. T. Roweis, editors, ICML, volume 307 of ACM International Conference Proceeding Series, pages 1096-1103. ACM, 2008
30. M. D. Zeiler and R. Fergus. Visualizing and Understanding Convolutional Networks. CoRR, abs/1311. 2901, 2013. 1, 7