M. A. Aizerman, E. M. Braverman, and L. I. Ronzonoér, Theoretical foundations of the potential function method in pattern recognition learning, Automation and Remote Control, vol.25, pp.821-83, 1964.

S. Avila, N. Thome, M. Cord, E. Valle, and A. Araújo, BOSSA: Extended bow formalism for image classification, 2011 18th IEEE International Conference on Image Processing, p.46, 2011.
DOI : 10.1109/ICIP.2011.6116268

URL : https://hal.archives-ouvertes.fr/hal-00625533

S. Avila, N. Thome, M. Cord, E. Valle, and A. Araújo, Pooling in image representation: The visual codeword point of view, Computer Vision and Image Understanding, vol.117, issue.5, pp.453-465, 2013.
DOI : 10.1016/j.cviu.2012.09.007

URL : https://hal.archives-ouvertes.fr/hal-01172709

H. B. Barlow, Possible Principles Underlying the Transformations of Sensory Messages, pp.217-234, 1961.
DOI : 10.7551/mitpress/9780262518420.003.0013

H. B. Barlow, Single Units and Sensation: A Neuron Doctrine for Perceptual Psychology?, Perception, vol.1, issue.2, pp.371-394, 1972.
DOI : 10.1068/p010371

H. B. Barlow, Unsupervised Learning, Neural Computation, vol.4, issue.3, pp.295-311, 1989.
DOI : 10.1007/BF00288907

H. Bay, A. Ess, T. Tuytelaars, V. Gool, and L. , Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, vol.110, issue.3, pp.346-359, 2008.
DOI : 10.1016/j.cviu.2007.09.014

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.738

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.
DOI : 10.1137/080716542

Y. Bengio, Learning Deep Architectures for AI, Machine Learning, pp.1-127, 2009.
DOI : 10.1561/2200000006

Y. Bengio and O. Delalleau, Justifying and Generalizing Contrastive Divergence, Neural Computation, vol.17, issue.6, pp.1601-1621, 2009.
DOI : 10.1145/1390156.1390290

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.334.5982

B. Bengio, Y. Lamblin, P. Popovici, D. Larochelle, and H. , Greedy layer-wise training of deep networks, Advances in Neural Information Processing Systems (NIPS). 4, pp.29-33, 2006.

Y. Bengio and Y. Lecun, Scaling learning algorithms towards AI, Large-Scale Kernel Machines, 2007.

S. Bileschi, M. Riesenhuber, T. Poggio, T. Serre, and L. Wolf, Robust object recognition with cortex-like mechanisms, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, pp.411-426, 2007.

D. Blackwell, Conditional Expectation and Unbiased Sequential Estimation, The Annals of Mathematical Statistics, vol.18, issue.1, pp.105-110, 1947.
DOI : 10.1214/aoms/1177730497

O. Boiman, E. Shechtman, and M. Irani, In defense of Nearest-Neighbor based image classification, 2008 IEEE Conference on Computer Vision and Pattern Recognition, p.119, 2008.
DOI : 10.1109/CVPR.2008.4587598

W. Boos and D. Vogel, Sparsely connected, hebbian networks with strikingly large storage capacities, Neural Networks, vol.10, issue.4, pp.671-682, 1997.

A. Bordes, New Algorithms for Large-Scale Support Vector Machines, p.47, 2010.
URL : https://hal.archives-ouvertes.fr/tel-00464007

A. Bordes, L. Bottou, and P. Gallinari, SGD-QN: Careful quasi-Newton stochastic gradient descent, Journal of Machine Learning Research, vol.10, pp.1737-1754, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00750911

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992.
DOI : 10.1145/130385.130401

Y. Boureau, F. Bach, Y. Lecun, and J. Ponce, Learning mid-level features for recognition, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5539963

Y. Boureau, L. Roux, N. Bach, F. Ponce, J. Lecun et al., Ask the locals: Multi-way local pooling for image recognition, 2011 International Conference on Computer Vision, p.122, 2011.
DOI : 10.1109/ICCV.2011.6126555

URL : https://hal.archives-ouvertes.fr/hal-00646816

Y. Boureau, J. Ponce, and Y. Lecun, A theoretical analysis of feature pooling in vision algorithms, International Conference on Machine Learning (ICML), p.45, 2010.

H. Bourlard and Y. Kamp, Auto-association by multilayer perceptrons and singular value decomposition, Biological Cybernetics, vol.13, issue.4-5, pp.291-294, 1988.
DOI : 10.1121/1.395916

G. J. Burghouts and J. M. Geusebroek, Performance evaluation of local colour invariants, Computer Vision and Image Understanding, vol.113, issue.1, pp.48-62, 2009.
DOI : 10.1016/j.cviu.2008.07.003

K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman, The devil is in the details: an evaluation of recent feature encoding methods, Procedings of the British Machine Vision Conference 2011, pp.1-12, 2011.
DOI : 10.5244/C.25.76

H. Chen and A. F. Murray, Continuous restricted Boltzmann machine with an implementable training algorithm, IEE Proceedings of Vision, Image and Signal Processing, pp.153-158, 2003.
DOI : 10.1049/ip-vis:20030362

D. C. Cire¸sancire¸san, U. Meier, and J. Schmidhuber, Multi-column deep neural networks for image classification, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3642-3649, 2012.

R. Collobert and S. Bengio, Links between perceptrons, MLPs and SVMs, Twenty-first international conference on Machine learning , ICML '04, p.16, 2004.
DOI : 10.1145/1015330.1015415

C. Cortes and V. N. Vapnik, Support-vector networks, Machine Learning, pp.273-297, 1995.
DOI : 10.1007/BF00994018

A. Courville, J. Bergstra, and Y. Bengio, The spike and slab restricted Boltzmann machine, International Conference on Artificial Intelligence and Statistics (AIS- TATS), pp.233-241, 2011.

G. Cybenko, Approximations by superpositions of sigmoidal functions, Mathematics of Control, Signals, and Systems, pp.303-314, 1989.

G. Dahl, M. Ranzato, A. Rahman-mohamed, and G. E. Hinton, Phone recognition with the mean-covariance restricted Boltzmann machine, Advances in Neural Information Processing Systems (NIPS), pp.469-477, 2010.

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), p.120, 2005.
DOI : 10.1109/CVPR.2005.177

URL : https://hal.archives-ouvertes.fr/inria-00548512

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., Imagenet: A large-scale hierarchical image database, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 2009.

J. Feng, B. Ni, Q. Tian, Y. , and S. , Geometric ? p -norm feature pooling for image classification, IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 45, p.125, 2011.

P. Földiák, Learning Invariance from Transformation Sequences, Neural Computation, vol.88, issue.2, pp.194-200, 1991.
DOI : 10.1088/0954-898X/2/1/003

P. Földiák, Neural coding: non-local but explicit and conceptual, Current Biology, vol.19, issue.54, pp.53-77, 2009.

P. Földiák, Sparse and explicit neural coding, Principles of Neural Coding, pp.379-390, 2013.

P. Földiák and M. P. Young, Sparse coding in the primate cortex The Handbook of Brain Theory and Neural Networks, pp.1064-4068, 2002.

J. Fournier, M. Cord, and S. Philipp-foliguet, RETIN: A Content-Based Image Indexing and Retrieval System, Pattern Analysis & Applications, vol.4, issue.2-3, pp.153-173, 2001.
DOI : 10.1007/PL00014576

L. Franco, E. T. Rolls, N. C. Aggelopoulos, and J. M. Jerez, Neuronal selectivity, population sparseness, and ergodicity in the inferior temporal visual cortex, Biological Cybernetics, vol.256, issue.6, pp.96547-560, 2007.
DOI : 10.1007/s00422-007-0149-1

M. O. Franz, B. Scholkopf, H. A. Mallot, and H. H. Bulthoff, Where did I take that snapshot? Scene-based homing by image matching, Biological Cybernetics, vol.79, issue.3, pp.191-202, 1998.
DOI : 10.1007/s004220050470

W. J. Fu, Penalized regressions: The bridge versus the lasso, Journal of Computational and Graphical Statistics, vol.7, pp.397-416, 1998.

K. Fukushima, Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, vol.40, issue.4, pp.93-202, 1980.
DOI : 10.1007/BF00344251

J. Geusebroek, G. Burghouts, and A. Smeulders, The Amsterdam Library of Object Images, International Journal of Computer Vision, vol.61, issue.1, p.75, 2005.
DOI : 10.1023/B:VISI.0000042993.50813.60

H. Goh, ?. Ku´smierzku´smierz, J. Lim, N. Thome, and M. Cord, Learning invariant color features with sparse topographic restricted Boltzmann machines, 2011 18th IEEE International Conference on Image Processing, p.144, 2011.
DOI : 10.1109/ICIP.2011.6115657

URL : https://hal.archives-ouvertes.fr/hal-00716053

H. Goh, N. Thome, and M. Cord, Biasing restricted Boltzmann machines to Bibliography manipulate latent selectivity and sparsity, NIPS Workshop on Deep Learning and Unsupervised Feature Learning, p.144, 2010.

H. Goh, N. Thome, M. Cord, and J. Lim, Neuroscience-informed sparsity and selectivity in restricted Boltzmann machines. Poster presented at Decade of Mind VI Conference, p.144, 2010.

H. Goh, N. Thome, M. Cord, and J. Lim, Unsupervised and Supervised Visual Codes with Restricted Boltzmann Machines, European Conference on Computer Vision (ECCV). 101, p.144, 2012.
DOI : 10.1007/978-3-642-33715-4_22

URL : https://hal.archives-ouvertes.fr/hal-00816428

H. Goh, N. Thome, M. Cord, and J. Lim, Top-down regularization of deep belief networks, Advances in Neural Information Processing Systems (NIPS), p.144, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00947569

H. Goh, N. Thome, M. Cord, and J. Lim, Learning Deep Hierarchical Visual Feature Coding, IEEE Transactions on Neural Networks and Learning Systems, p.144, 2014.
DOI : 10.1109/TNNLS.2014.2307532

URL : https://hal.archives-ouvertes.fr/hal-01185465

B. Graham and D. Willshaw, Capacity and information efficiency of the associative net, Network: Computation in Neural Systems, vol.8, issue.1, pp.35-54, 1997.
DOI : 10.1088/0954-898X_8_1_005

K. Grauman and T. Darrell, The pyramid match kernel: discriminative classification with sets of image features, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.1458-1465, 2005.
DOI : 10.1109/ICCV.2005.239

G. Griffin, A. Holub, and P. Perona, Caltech-256 object category dataset, California Institute of Technology, vol.6, issue.36, p.113, 2007.

C. G. Gross, Genealogy of the ???Grandmother Cell???, The Neuroscientist, vol.73, issue.5, pp.512-518, 2002.
DOI : 10.1177/107385802237175

G. E. Hinton, Training Products of Experts by Minimizing Contrastive Divergence, Neural Computation, vol.22, issue.8, pp.1771-1800, 2002.
DOI : 10.1162/089976600300015385

G. E. Hinton, Learning multiple layers of representation, Trends in Cognitive Sciences, vol.11, issue.10, pp.428-434, 2007.
DOI : 10.1016/j.tics.2007.09.004

G. E. Hinton, To recognize shapes, first learn to generate images, Computational Neuroscience: Theoretical Insights into Brain Function, pp.535-547, 2007.
DOI : 10.1016/S0079-6123(06)65034-6

G. E. Hinton, A Practical Guide to Training Restricted Boltzmann Machines, p.147, 2010.
DOI : 10.1073/pnas.79.8.2554

G. E. Hinton, J. L. Mcclelland, R. , and D. E. , Distributed representations, Parallel Distributed Processing, pp.77-109, 1986.

G. E. Hinton, S. Osindero, and Y. Teh, A Fast Learning Algorithm for Deep Belief Nets, Neural Computation, vol.18, issue.7, pp.1527-1554, 2006.
DOI : 10.1162/jmlr.2003.4.7-8.1235

G. E. Hinton, D. Peter, B. J. Frey, N. , and R. M. , The "wake-sleep" algorithm for unsupervised neural networks, Science, vol.268, issue.5214, pp.2681158-1161, 1995.
DOI : 10.1126/science.7761831

G. E. Hinton and R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks, Science, vol.313, issue.5786, pp.504-507, 2006.
DOI : 10.1126/science.1127647

G. E. Hinton and T. Sejnowski, Learning and relearning in Boltzmann machines, Parallel Distributed Processing, pp.282-317, 1986.

J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of Sciences of the USA, pp.2554-2558, 1982.

K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Networks, vol.4, issue.2, pp.251-257, 1991.
DOI : 10.1016/0893-6080(91)90009-T

D. H. Hubel and T. N. Wiesel, Receptive fields, binocular interaction and functional architecture in the cat's visual cortex, The Journal of Physiology, vol.160, issue.1, pp.106-154, 1962.
DOI : 10.1113/jphysiol.1962.sp006837

A. Hyvärinen and P. O. Hoyer, A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images, Vision Research, vol.41, issue.18, pp.2413-2423, 2001.
DOI : 10.1016/S0042-6989(01)00114-6

A. Hyvärinen, P. O. Hoyer, and M. Inki, Topographic Independent Component Analysis, Neural Computation, vol.18, issue.10, pp.1527-1558, 2001.
DOI : 10.1016/S0013-4694(97)00042-8

H. Jégou, M. Douze, C. Schmid, and P. Pérez, Aggregating local descriptors into a compact image representation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.42, 2010.
DOI : 10.1109/CVPR.2010.5540039

Y. Jia, C. Huang, D. , and T. , Beyond spatial pyramids: Receptive field learning for pooled image features, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3370-3377, 2012.

Z. Jiang, Z. Lin, D. , and L. S. , Learning a discriminative dictionary for sparse coding via label consistent K-SVD, CVPR 2011, p.124, 2011.
DOI : 10.1109/CVPR.2011.5995354

F. Jurie and B. Triggs, Creating efficient codebooks for visual recognition, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.604-610, 2005.
DOI : 10.1109/ICCV.2005.66

URL : https://hal.archives-ouvertes.fr/inria-00548511

Y. Karklin and M. Lewicki, Emergence of complex cell properties by learning to generalize in natural scenes, Nature, vol.9, issue.7225, p.28, 2008.
DOI : 10.1016/j.conb.2004.07.007

K. Kavukcuoglu, M. Ranzato, R. Fergus, and Y. Lecun, Learning invariant features through topographic filter maps, 2009 IEEE Conference on Computer Vision and Pattern Recognition, p.119, 2009.
DOI : 10.1109/CVPR.2009.5206545

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.9635

K. Kavukcuoglu, P. Sermanet, Y. Boureau, K. Gregor, M. Mathieu et al., Learning convolutional feature hierarchies for visual recognition, Advances in Neural Information Processing Systems (NIPS), pp.1090-1098, 2010.

Y. Ke and R. Sukthankar, PCA-SIFT: a more distinctive representation for local image descriptors, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.506-513, 2004.

T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol.13, issue.1, pp.59-69, 1982.
DOI : 10.1007/BF00337288

J. Konorski, Integrative activity of the brain: an interdisciplinary approach, p.52, 1967.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems (NIPS). 5, p.107, 2012.

S. Kullback and R. Leibler, On Information and Sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.79-86, 1951.
DOI : 10.1214/aoms/1177729694

H. Larochelle and Y. Bengio, Classification using discriminative restricted Boltzmann machines, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.536-543, 2008.
DOI : 10.1145/1390156.1390224

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.8286

H. Larochelle, Y. Bengio, J. Louradour, and P. Lamblin, Exploring strategies for training deep neural networks, Journal of Machine Learning Research, vol.10, issue.29, pp.1-40, 2009.

H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio, An empirical evaluation of deep architectures on problems with many factors of variation, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.473-480, 2007.
DOI : 10.1145/1273496.1273556

M. Law, N. Thome, and M. Cord, Hybrid Pooling Fusion in the BoW Pipeline, 2012.
DOI : 10.1007/978-3-642-33885-4_36

URL : https://hal.archives-ouvertes.fr/hal-01282471

S. Lazebnik and M. Raginsky, Supervised Learning of Quantizer Codebooks by Information Loss Minimization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.7, pp.1294-1309, 2009.
DOI : 10.1109/TPAMI.2008.138

S. Lazebnik, C. Schmid, and J. Ponce, Semi-Local Affine Parts for Object Recognition, Procedings of the British Machine Vision Conference 2004, pp.779-788, 2004.
DOI : 10.5244/C.18.98

URL : https://hal.archives-ouvertes.fr/inria-00548542

S. Lazebnik, C. Schmid, and J. Ponce, Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.113-116, 2006.
DOI : 10.1109/CVPR.2006.68

URL : https://hal.archives-ouvertes.fr/inria-00548585

Q. Le, J. Ngiam, Z. Chen, D. J. Hao-chia, P. W. Koh et al., Tiled convolutional neural networks, Advances in Neural Information Processing Systems (NIPS), pp.1279-1287, 2010.

Q. V. Le, R. Monga, M. Devin, G. Corrado, K. Chen et al., Building high-level features using large scale unsupervised learning, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2012.
DOI : 10.1109/ICASSP.2013.6639343

L. Roux, N. Bengio, and Y. , Representational Power of Restricted Boltzmann Machines and Deep Belief Networks, Neural Computation, vol.20, issue.6, pp.1631-1649, 2008.
DOI : 10.1016/S0364-0213(85)80012-4

L. Roux, N. Bengio, and Y. , Deep Belief Networks Are Compact Universal Approximators, Neural Computation, vol.1, issue.8, pp.2192-2207, 2010.
DOI : 10.1145/1390156.1390294

Y. Lecun, Une procédure d'apprentissage pour réseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks), Cognitiva 85, pp.599-604, 1985.

Y. Bibliography-lecun, B. Boser, J. Denker, D. Henderson, R. Howard et al., Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol.1, issue.4, pp.541-551, 1989.
DOI : 10.1007/BF00133697

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, pp.2278-2324, 1998.
DOI : 10.1109/5.726791

H. Lee, A. Battle, R. Raina, and A. Y. Ng, Efficient sparse coding algorithms, Advances in Neural Information Processing Systems (NIPS), pp.801-808, 2007.

H. Lee, C. Ekanadham, and A. Ng, Sparse deep belief net model for visual area V2, Advances in Neural Information Processing Systems (NIPS), pp.873-880, 2008.

H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.609-616, 2009.
DOI : 10.1145/1553374.1553453

B. Leibe, A. Leonardis, and B. Schiele, Robust Object Detection with Interleaved Categorization and Segmentation, International Journal of Computer Vision, vol.73, issue.2, pp.1-3259, 2008.
DOI : 10.1007/s11263-007-0095-3

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.111.464

W. Levy and R. Baxter, Energy Efficient Neural Codes, Neural Computation, vol.14, issue.4, pp.531-543, 1996.
DOI : 10.1016/0006-8993(85)90226-4

Y. Linde, A. Buzo, and R. M. Gray, An Algorithm for Vector Quantizer Design, IEEE Transactions on Communications, vol.28, issue.1, pp.84-94, 1980.
DOI : 10.1109/TCOM.1980.1094577

J. Liu and M. Shah, Scene Modeling Using Co-Clustering, 2007 IEEE 11th International Conference on Computer Vision, pp.1-7, 2007.
DOI : 10.1109/ICCV.2007.4408866

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.76.4839

L. Liu, L. Wang, and X. Liu, In defense of soft-assignment coding, International Conference on Computer Vision (ICCV, p.118, 2011.

S. Lloyd, Least squares quantization in PCM, IEEE Transactions on Information Theory, vol.28, issue.2, pp.129-137, 1982.
DOI : 10.1109/TIT.1982.1056489

D. Lowe, Object recognition from local scale-invariant features, Proceedings of the Seventh IEEE International Conference on Computer Vision, pp.1150-1157, 1999.
DOI : 10.1109/ICCV.1999.790410

W. Ma and B. Manjunath, Netra: a toolbox for navigating large image databases, International Conference on Image Processing (ICIP), pp.568-571, 1997.

J. Mairal, Sparse Coding for Machine Learning, Image Processing and Computer Vision, p.43, 2010.
URL : https://hal.archives-ouvertes.fr/tel-00595312

J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman, Supervised dictionary learning, Advances in Neural Information Processing Systems (NIPS), p.44, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00322431

D. Marr, Early Processing of Visual Information, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.275, issue.942, pp.483-519, 1976.
DOI : 10.1098/rstb.1976.0090

W. Mcculloch and W. Pitts, A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, vol.5, issue.4, pp.115-133, 1943.
DOI : 10.1007/BF02478259

R. Memisevic and G. E. Hinton, Unsupervised Learning of Image Transformations, 2007 IEEE Conference on Computer Vision and Pattern Recognition, p.27, 2007.
DOI : 10.1109/CVPR.2007.383036

R. Memisevic and G. E. Hinton, Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines, Neural Computation, vol.17, issue.6, pp.1473-92, 2010.
DOI : 10.1007/3-540-47969-4_30

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.10, issue.39, pp.1615-1630, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548227

T. M. Mitchell, The need for biases in learning generalizations, p.49, 1980.

G. J. Mitchison and R. M. Durbin, Bounds on the learning capacity of some multi-layer networks, Biological Cybernetics, vol.60, issue.5, pp.345-356, 1989.
DOI : 10.1007/BF00204772

A. Mohamed, G. E. Dahl, and G. Hinton, Acoustic Modeling Using Deep Belief Networks, IEEE Transactions on Audio, Speech, and Language Processing, vol.20, issue.1, pp.14-22, 2012.
DOI : 10.1109/TASL.2011.2109382

J. Mutch and D. Lowe, Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields, International Journal of Computer Vision, vol.5, issue.7, pp.45-47, 2008.
DOI : 10.1007/s11263-007-0118-0

V. Nair and G. E. Hinton, 3d object recognition with deep belief nets, Advances in Neural Information Processing Systems (NIPS), pp.1339-1347, 2009.

B. Neal and R. , Connectionist learning of belief networks, Artificial Intelligence, vol.56, issue.1, pp.71-113, 1992.
DOI : 10.1016/0004-3702(92)90065-6

R. M. Neal and G. E. Hinton, A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants, Learning in graphical models, pp.355-368, 1998.
DOI : 10.1007/978-94-011-5014-9_12

D. Nister and H. Stewenius, Scalable Recognition with a Vocabulary Tree, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.2161-2168, 2006.
DOI : 10.1109/CVPR.2006.264

M. Norouzi, M. Ranjbar, and G. Mori, Stacks of convolutional Restricted Boltzmann Machines for shift-invariant feature learning, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.2735-2742, 2009.
DOI : 10.1109/CVPR.2009.5206577

A. B. Novikoff, On convergence proofs on perceptrons, Symposium on the Mathematical Theory of Automata, pp.615-622, 1962.

E. Nowak, F. Jurie, and B. Triggs, Sampling Strategies for Bag-of-Features Image Classification, European Conference on Computer Vision (ECCV), pp.490-503, 2006.
DOI : 10.1007/11744085_38

URL : https://hal.archives-ouvertes.fr/hal-00203752

A. Oliva and A. Torralba, Modeling the shape of the scene: A holistic representation of the spatial envelope, International Journal of Computer Vision, vol.42, issue.3, pp.145-175, 2001.
DOI : 10.1023/A:1011139631724

G. Oliveira, E. Nascimento, A. Vieira, and M. F. Campos, Sparse Spatial Coding: A novel approach for efficient and accurate object recognition, 2012 IEEE International Conference on Robotics and Automation, pp.2592-2598, 2012.
DOI : 10.1109/ICRA.2012.6224785

A. Olmos and F. Kingdom, McGill calibrated colour image database, 2004.

B. A. Olshausen, Sparse codes and spikes, Probabilistic Models of the Brain: Perception and Neural Function, pp.257-272, 2001.

B. A. Olshausen and D. J. Field, Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Nature, vol.381, issue.6583, pp.381607-66, 1996.
DOI : 10.1038/381607a0

C. Perpiñán and G. E. Hinton, On contrastive divergence learning, International Workshop on Artificial Intelligence and Statistics (AISTATS), pp.59-66, 2005.

F. Perronnin and C. Dance, Fisher Kernels on Visual Vocabularies for Image Categorization, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383266

F. Perronnin, J. Sánchez, and T. Mensink, Improving the Fisher Kernel for Large-Scale Image Classification, European Conference on Computer Vision (ECCV), pp.143-156, 2010.
DOI : 10.1007/978-3-642-15561-1_11

URL : https://hal.archives-ouvertes.fr/inria-00548630

J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, Lost in quantization: Improving particular object retrieval in large scale image databases, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587635

J. Ponce, T. Berg, M. Everingham, D. Forsyth, M. Hebert et al., Dataset Issues in Object Recognition, Toward Category-Level Object Recognition of Lecture Notes in Computer Science (LNCS), pp.29-48, 2006.
DOI : 10.1007/11957959_2

URL : https://hal.archives-ouvertes.fr/inria-00548595

R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng, Self-taught learning, Proceedings of the 24th international conference on Machine learning, ICML '07, p.124, 2007.
DOI : 10.1145/1273496.1273592

A. Rakotomamonjy, Direct Optimization of the Dictionary Learning Problem, IEEE Transactions on Signal Processing, vol.61, issue.22, p.17, 2013.
DOI : 10.1109/TSP.2013.2278158

URL : https://hal.archives-ouvertes.fr/hal-00850248

M. Ranzato, Y. Boureau, S. Chopra, and Y. Lecun, A unified energy-based framework for unsupervised learning, International Conference on Artificial Intelligence and Statistics (AISTATS), p.44, 2007.

M. Ranzato and G. E. Hinton, Modeling pixel means and covariances using factorized third-order boltzmann machines, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2551-2558, 2010.
DOI : 10.1109/CVPR.2010.5539962

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.2794

M. Ranzato, V. Mnih, and G. E. Hinton, Generating more realistic images using gated MRF's, Advances in Neural Information Processing Systems (NIPS), pp.2002-2010, 2010.

M. Ranzato, C. Poultney, S. Chopra, and Y. Lecun, Efficient learning of sparse representations with an energy-based model Hierarchical models of object recognition in cortex, Advances in Neural Information Processing Systems (NIPS), pp.1137-11441019, 1999.

H. Robbins and S. Monro, A Stochastic Approximation Method, The Annals of Mathematical Statistics, vol.22, issue.3, pp.400-407, 1951.
DOI : 10.1214/aoms/1177729586

E. T. Rolls and T. Milward, A Model of Invariant Object Recognition in the Visual System: Learning Rules, Activation Functions, Lateral Inhibition, and Information-Based Performance Measures, Neural Computation, vol.2, issue.11, pp.2547-2572, 2000.
DOI : 10.1016/S0301-0082(96)00054-8

E. T. Rolls and S. M. Stringer, Invariant object recognition in the visual system with error correction and temporal difference learning, Network: Computation in Neural Systems, vol.51, issue.2, pp.111-129, 2001.
DOI : 10.1162/neco.1997.9.4.883

E. T. Rolls and A. Treves, The relative advantages of sparse versus distributed encoding for associative neuronal networks in the brain, Network: Computation in Neural Systems, vol.1, issue.4, pp.407-421, 1990.
DOI : 10.1088/0954-898X_1_4_002

F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, pp.386-408, 1958.
DOI : 10.1037/h0042519

D. E. Rumelhart, G. E. Hinton, W. , and R. J. , Learning representations by back-propagating errors, Nature, vol.85, issue.6088, pp.533-536, 1986.
DOI : 10.1038/323533a0

R. Salakhutdinov and G. E. Hinton, Learning a nonlinear embedding by preserving class neighbourhood structure, International Conference on Artificial Intelligence and Statistics (AISTATS), p.100, 2007.

R. Salakhutdinov and G. E. Hinton, Semantic hashing, International Journal of Approximate Reasoning, vol.50, issue.7, pp.969-978, 2009.
DOI : 10.1016/j.ijar.2008.11.006

URL : http://doi.org/10.1016/j.ijar.2008.11.006

R. Salakhutdinov and G. E. Hinton, An Efficient Learning Procedure for Deep Boltzmann Machines, Neural Computation, vol.17, issue.8, pp.1967-2006, 2012.
DOI : 10.1080/17442509908834179

R. Salakhutdinov, A. Mnih, and G. Hinton, Restricted Boltzmann machines for collaborative filtering, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.791-798, 2007.
DOI : 10.1145/1273496.1273596

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.6034

G. Salton and M. J. Mcgill, Introduction to Modern Information Retrieval, 1983.

C. Schmid, R. Mohr, and C. Bauckhage, Evaluation of interest point detectors, International Journal of Computer Vision, vol.37, issue.2, pp.151-172, 2000.
DOI : 10.1023/A:1008199403446

URL : https://hal.archives-ouvertes.fr/inria-00548302

T. J. Sejnowski, Higher-order Boltzmann machines, AIP Conference Proceedings, pp.398-403, 1987.
DOI : 10.1063/1.36246

G. Sharma, F. Jurie, and C. Schmid, Discriminative spatial saliency for image classification, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3506-3513, 2012.
DOI : 10.1109/CVPR.2012.6248093

URL : https://hal.archives-ouvertes.fr/hal-00714311

J. Sivic and A. Zisserman, Video Google: a text retrieval approach to object matching in videos, Proceedings Ninth IEEE International Conference on Computer Vision, p.41, 2003.
DOI : 10.1109/ICCV.2003.1238663

P. Smolensky, Information processing in dynamical systems: Foundations of harmony theory, Parallel Distributed Processing, pp.194-281, 1986.

K. Sohn, D. Y. Jung, H. Lee, I. Hero, and A. , Efficient learning of sparse, distributed, convolutional feature representations for object recognition, International Conference on Computer Vision (ICCV, p.44, 2011.

K. Sohn, D. Y. Jung, H. Lee, I. Hero, and A. , Efficient learning of sparse, distributed, convolutional feature representations for object recognition, International Conference on Computer Vision (ICCV, p.125, 2011.

I. Sutskever and G. E. Hinton, Learning multilevel distributed representations for high-dimensional sequences, International Conference on Artificial Intelligence and Statistics (AISTATS), p.30, 2007.

I. Sutskever and G. E. Hinton, Deep, Narrow Sigmoid Belief Networks Are Universal Approximators, Neural Computation, vol.20, issue.11, pp.2629-2665, 2008.
DOI : 10.1038/323533a0

M. J. Swain and D. H. Ballard, Indexing via color histograms, [1990] Proceedings Third International Conference on Computer Vision, pp.390-393, 1990.
DOI : 10.1109/ICCV.1990.139558

M. J. Swain and D. H. Ballard, Color indexing, International Journal of Computer Vision, vol.31, issue.1, pp.11-32, 1991.
DOI : 10.1007/BF00130487

K. Swersky, B. Chen, B. Marlin, and N. De-freitas, A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets, 2010 Information Theory and Applications Workshop (ITA), p.26, 2010.
DOI : 10.1109/ITA.2010.5454138

A. Szlam, K. Gregor, and Y. Lecun, Fast Approximations to Structured Sparse Coding and Applications to Object Classification, European Conference on Computer Bibliography Vision, pp.200-213, 2012.
DOI : 10.1007/978-3-642-33715-4_15

G. W. Taylor and G. E. Hinton, Factored conditional restricted Boltzmann Machines for modeling motion style, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.1025-1032, 2009.
DOI : 10.1145/1553374.1553505

Y. W. Teh, M. Welling, S. Osindero, and G. E. Hinton, Energy-based models for sparse overcomplete representations, Journal of Machine Learning Research, vol.4, issue.7 8, pp.1235-1260, 2004.

C. Theriault, N. Thome, and M. Cord, Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis, 2013 IEEE Conference on Computer Vision and Pattern Recognition, p.78, 2013.
DOI : 10.1109/CVPR.2013.336

URL : https://hal.archives-ouvertes.fr/hal-01216646

C. Theriault, N. Thome, and M. Cord, Extended Coding and Pooling in the HMAX Model, IEEE Transactions on Image Processing, vol.22, issue.2, 2013.
DOI : 10.1109/TIP.2012.2222900

URL : https://hal.archives-ouvertes.fr/hal-01185467

T. Tieleman, Training restricted Boltzmann machines using approximations to the likelihood gradient, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.1064-1071, 2008.
DOI : 10.1145/1390156.1390290

A. Torralba, Small codes and large image databases for recognition, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587633

A. Treves and E. T. Rolls, What determines the capacity of autoassociative memories in the brain? Network: Computation in Neural Systems, pp.371-397, 1991.

T. Tuytelaars, M. Fritz, K. Saenko, D. , and T. , The NBNN kernel, 2011 International Conference on Computer Vision, p.119, 2011.
DOI : 10.1109/ICCV.2011.6126449

T. Tuytelaars and K. Mikolajczyk, Local invariant feature detectors: a survey. Foundations and Trends in Computer Graphics and Vision, pp.177-280, 2008.

K. E. Van-de-sande, T. Gevers, and C. G. Snoek, Evaluating Color Descriptors for Object and Scene Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1582-1596, 2010.
DOI : 10.1109/TPAMI.2009.154

J. Van-de-weijer and C. Schmid, Coloring Local Feature Extraction, European Conference on Computer Vision (ECCV), pp.334-348, 2006.
DOI : 10.1002/col.10049

URL : https://hal.archives-ouvertes.fr/inria-00548576

J. Van-gemert, C. Veenman, A. Smeulders, J. Geusebroek, and V. N. , Visual word ambiguity The nature of statistical learning theory, Vapnik, pp.47-127, 1995.

V. N. Vapnik and A. Lerner, Pattern recognition using generalized portrait method. Automation and Remote Control, pp.778-780, 1963.

A. Vattani, k-means Requires Exponentially Many Iterations Even in the Plane, Discrete & Computational Geometry, vol.51, issue.3, pp.596-616, 2011.
DOI : 10.1007/s00454-011-9340-1

V. Viitaniemi and J. Laaksonen, Experiments on Selection of Codebooks for Local Image Feature Histograms, International Conference on Visual Information Systems (VISUAL), pp.126-137, 2008.
DOI : 10.1007/978-3-540-85891-1_16

P. Vincent, H. Larochelle, Y. Bengio, and P. Manzagol, Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.1096-1103, 2008.
DOI : 10.1145/1390156.1390294

G. Wallis and E. T. Rolls, INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM, Progress in Neurobiology, vol.51, issue.2, pp.167-194
DOI : 10.1016/S0301-0082(96)00054-8

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang et al., Locality-constrained Linear Coding for image classification, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.117-118, 2010.
DOI : 10.1109/CVPR.2010.5540018

M. Welling, S. Osindero, and G. E. Hinton, Learning sparse topographic representations with products of student-t distributions, Advances in Neural Information Processing Systems (NIPS), pp.1359-1366, 2003.

M. Welling, M. Rosen-zvi, and G. E. Hinton, Exponential family harmoniums with an application to information retrieval, Advances in Neural Information Processing Systems (NIPS), pp.1481-1488, 2005.

P. Wellman and M. Henrion, Explaining 'explaining away', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.3, pp.287-292, 1993.
DOI : 10.1109/34.204911

B. Willmore and D. J. Tolhurst, Characterizing the sparseness of neural codes, Network: Computation in Neural Systems, vol.2, issue.3, pp.255-270, 2001.
DOI : 10.1080/net.12.3.255.270

L. Wiskott and T. Sejnowski, Slow Feature Analysis: Unsupervised Learning of Invariances, Neural Computation, vol.13, issue.11, pp.715-770, 2002.
DOI : 10.1016/S0301-0082(96)00054-8

J. Wolf, W. Burgard, and H. Burkhardt, Robust vision-based localization for mobile robots using an image retrieval system based on invariant features, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002.
DOI : 10.1109/ROBOT.2002.1013387