Are sparse representations really relevant for image classification?

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

Volumn , Issue , 2011, Pages 1545-1552

  Rigamonti, Roberto ^a   Brown, Matthew A ^a   Lepetit, Vincent ^a  

^a EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

FILTER BANKS; OBJECT RECOGNITION;

AND FILTERS; COMPUTATIONAL COSTS; IMAGE DESCRIPTOR; MODULAR ARCHITECTURES; RECOGNITION RATES; SPARSE REPRESENTATION; SPARSITY CONSTRAINTS; VISUAL CORTEXES;

IMAGE CLASSIFICATION;

EID: 80052904079     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995313     Document Type: Conference Paper

References (37)

1. F. Bergeaud and S. Mallat. Matching Pursuit of Images. In ICIP, 1995.
2. Y.-L. Boureau, F. Bach, Y. LeCun, and J. Ponce. Learning Mid-Level Features for Recognition. In CVPR, 2010.
3. Y.-L. Boureau, J. Ponce, and Y. LeCun. A Theoretical Analysis of Feature Pooling in Visual Recognition. In ICML, 2010.
4. M. Brown, G. Hua, and S. Winder. Discriminative Learning of Local Image Descriptors. PAMI, 2010.
5. I. Daubechies, M. Defrise, and C. D. Mol. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. CPAM, 2004.
6. D. L. Donoho. Compressed Sensing. TIT, 2006.
7. 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. In CVPR, 2004.
8. G. E. Hinton. Learning to Represent Visual Input. RSTB, 2010.
9. G. Hua, M. Brown, and S. Winder. Discriminant Embedding for Local Image Descriptors. In ICCV, 2007.
10. D. H. Hubel and T. N. Wiesel. Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex. JPHYSIO, 1962.
11. A. Hyvärinen, J. Hurri, and P. O. Hoyer. Natural Image Statistics. Springer-Verlag, 2009.
12. K. Jarrett, K. Kavukcuoglu, M. A. Ranzato, and Y. LeCun. What Is the Best Multi-Stage Architecture for Object Recognition? In ICCV, 2009.
13. K. Kavukcuoglu, M. A. Ranzato, and Y. LeCun. Fast Inference in Sparse Coding Algorithms With Applications to Object Recognition. Technical report, NYU, 2008.
14. A. Krizhevsky. Learning Multiple Layers of Features from Tiny Images. Master's thesis, 2009.
15. A. Krizhevsky. Convolutional Deep Belief Networks on CIFAR-10. Technical report, UOFT, 2010.
16. S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In CVPR, 2006.
17. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-Based Learning Applied to Document Recognition. PIEEE, 1998.
18. H. Lee, C. Ekanadham, and A. Y. Ng. Sparse Deep Belief Net Model for Visual Area V2. In NIPS, 2007.
19. H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations. In ICML, 2009.
20. T. Leung and J. Malik. Representing and Recognizing the Visual Appearance of Materials Using Three-Dimensional Textons. IJCV, 2001.
21. D. G. Lowe. Distinctive Image Features from Scale-Invariants Keypoints. IJCV, 2004.
22. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Discriminative Learned Dictionaries for Local Image Analysis. In CVPR, 2008.
23. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Non-Local Sparse Models for Image Restoration. In ICCV, 2009.
24. B. A. Olshausen and D. J. Field. Sparse Coding With an Overcomplete Basis Set: A Strategy Employed by V1? VISR, 1997.
25. N. Pinto, D. D. Cox, and J. J. DiCarlo. Why Is Real-World Visual Object Recognition Hard? PLoS, 2008.
26. M. A. Ranzato, Y.-L. Boureau, and Y. LeCun. Sparse Feature Learning for Deep Belief Networks. In NIPS, 2007.
27. M. A. Ranzato and G. E. Hinton. Modeling Pixel Means and Covariances Using Factorized Third-Order Boltzmann Machines. In CVPR, 2010.
28. M. A. Ranzato, F.-J. Huang, Y. Boureau, and Y. LeCun. Unsupervised Learning of Invariant Feature Hierarchies With Applications to Object Recognition. In CVPR, 2007.
29. M. A. Ranzato, C. Poultney, S. Chopra, and Y. LeCun. Efficient Learning of Sparse Representations With an Energy-Based Model. In NIPS, 2006.
30. T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio. Robust Object Recognition With Cortex-Like Mechanisms. PAMI, 2007.
31. E. Tola, V. Lepetit, and P. Fua. DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo. PAMI, 2010.
32. A. Torralba, R. Fergus, and W. T. Freeman. 80 Million Tiny Images: A Large Dataset for Non-Parametric Object and Scene Recognition. PAMI, 2008.
33. W. E. Vinje and J. L. Gallant. Sparse Coding and Decorrelation in Primary Visual Cortex During Natural Vision. SCIENCE, 2000.
34. J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan. Sparse Representation for Computer Vision and Pattern Recognition. PIEEE, 2010.
35. J. Yang, K. Yu, Y. Gong, and T. Huang. Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification. In CVPR, 2009.
36. K. Yu and T. Zhang. Improved Local Coordinate Coding Using Local Tangents. In ICML, 2010.
37. M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus. Deconvolutional Networks. In CVPR, 2010.