B. Nicolas, Irim at TRECVID 2014 : Semantic indexing and instance search, Proceedings of TRECVID, 2014.

D. Thibaut, P. David, T. Nicolas, and C. Matthieu, Semantic pooling for image categorization using multiple kernel learning, 2014.

D. Thibaut, T. Nicolas, C. Matthieu, and P. David, Incremental learning of latent structural svm for weakly supervised image classification, 2014.

F. Jerome, P. David, and P. Gosselin, Dimensionality reduction in decentralized networks by Gossip aggregation of principal components analyzers, pp.171-176

N. Romain, P. David, and P. Gosselin, Dimensionality reduction of visual features using sparse projectors for content-based image retrieval, IEEE Int. Conf. on Image Processing (ICIP). 2014, pp.2192-2196

N. Romain, P. David, and P. Gosselin, Efficient Metric Learning Based Dimension Reduction Using Sparse Projectors For Image Near Duplicate Retrieval, 2014.

N. Romain, P. David, and P. Gosselin, Evaluation of second-order visual features for land-use classification, Content-Based Multimedia Indexing (CBMI), 2014 12th International Workshop on. IEEE. 2014, pp.1-5

P. David and F. Inbar, Second order model deviations of local Gabor features for texture classification, Signals, Systems and Computers, 2014 48th Asilomar Conference on. IEEE. 2014, pp.917-920

N. Romain, P. David, and P. Gosselin, Using Spatial Pyramids with Compacted VLAT for Image Categorization

P. David, T. Nicolas, C. Matthieu, and R. Et-alain, Learning geometric combinations of Gaussian kernels with alternating Quasi-Newton algorithm, pp.79-84

P. David and P. Gosselin, Improving Image Similarity With Vectors of Locally Aggregated Tensors, Image Processing (ICIP) 18th IEEE International Conference on, p.669, 2011.

P. David, T. Nicolas, and C. Matthieu, An efficient system for combining complementary kernels in complex visual categorization tasks, Image Processing (ICIP) 17th IEEE International Conference on. IEEE, pp.3877-3880, 2010.

P. David, C. Matthieu, and V. Eduardo, Study of SIFT Descriptors for Image Matching based Localization in Urban Street View Context " . In : CMRT09 -CityModels, Roads and Traffic, GITC, pp.193-198, 2009.

V. Eduardo, P. David, and C. Matthieu, Geometric consistency checking for local-descriptor based document retrieval, Proceedings of the 9th ACM symposium on Document engineering, pp.135-138, 2009.

P. David, R. Arnaud, and C. Matthieu, Image retrieval over networks : Ant algorithm for long term active learning, Content-Based Multimedia Indexing, pp.439-445, 2008.

P. David, R. Arnaud, and C. Matthieu, Long term learning for image retrieval over networks, Image Processing 15th IEEE International Conference on, pp.929-932, 2008.

P. David, C. Matthieu, and R. Arnaud, Cbir in distributed databases using a multi-agent system, Image Processing IEEE International Conference on. IEEE, pp.3205-3208, 2006.

P. David, R. Arnaud, and C. Matthieu, Performances of mobile-agents for interactive image retrieval, Proceedings of the 2006 IEEEACM International Conference on Web Intelligence, pp.581-586, 2006.

R. Arnaud, P. David, and C. Matthieu, Ant-like mobile agents for Content- Based Image Retrieval in distributed databases, p.29, 2005.

D. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94
URL : http://www.cs.ubc.ca/~lowe/papers/ijcv03.ps

A. Gionis, P. Indyk, and R. Motwani, Similarity search in high dimensions via hashing, Proceedings of the 25th International Conference on Very Large Data Bases, ser. VLDB '99, pp.518-529, 1999.

H. Lejsek, B. T. Jónsson, and L. Amsaleg, NV-Tree, Proceedings of the 1st ACM International Conference on Multimedia Retrieval, ICMR '11, pp.541-548, 2011.
DOI : 10.1145/1991996.1992050
URL : https://hal.archives-ouvertes.fr/hal-00644939

H. Jegou, M. Douze, and C. Schmid, Improving Bag-of-Features for Large Scale Image Search, International Journal of Computer Vision, vol.42, issue.3, pp.316-336, 2010.
DOI : 10.1007/s11263-009-0285-2
URL : https://hal.archives-ouvertes.fr/inria-00514760

J. Shawe-taylor and N. Cristianini, Kernel methods for Pattern Analysis, 2004.
DOI : 10.1017/CBO9780511809682

S. Lyu, Mercer kernels for object recognition with local features, IEEE International Conference on Computer Vision and Pattern Recognition, pp.223-229, 2005.

P. Gosselin, M. Cord, and S. Philipp-foliguet, Kernels on bags for multi-object database retrieval, Proceedings of the 6th ACM international conference on Image and video retrieval, CIVR '07, pp.226-231, 2007.
DOI : 10.1145/1282280.1282317

F. Precioso, M. Cord, D. Gorisse, and N. Thome, Efficient bag-offeature kernel representation for image similarity search, International Conference on Image Processing, pp.109-112, 2011.
DOI : 10.1109/icip.2011.6115618

J. Sivic and A. Zisserman, Video Google: a text retrieval approach to object matching in videos, Proceedings Ninth IEEE International Conference on Computer Vision, pp.1470-1477, 2003.
DOI : 10.1109/ICCV.2003.1238663

S. Avila, N. Thome, M. Cord, E. Valle, A. De et al., BOSSA: Extended bow formalism for image classification, 2011 18th IEEE International Conference on Image Processing, 2011.
DOI : 10.1109/ICIP.2011.6116268
URL : https://hal.archives-ouvertes.fr/hal-00625533

J. C. Van-gemert, J. Geusebroek, C. J. Veenman, and A. W. Smeulders, Kernel Codebooks for Scene Categorization, ECCV 2008, PART III, pp.696-709, 2008.
DOI : 10.1007/978-1-4899-3324-9

J. Yang, K. Yu, Y. Gong, and T. Huang, Linear spatial pyramid matching using sparse coding for image classification, IEEE Conference on Computer Vision and Pattern Recognition(CVPR, 2009.

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang et al., Localityconstrained linear coding for image classification, IEEE International Conference on Computer Vision and Pattern Recognition, pp.3360-3367, 2010.

F. Perronnin, J. Sánchez, and T. Mensink, Improving the Fisher Kernel for Large-Scale Image Classification, European Conference on Computer Vision, pp.143-156, 2010.
DOI : 10.1007/978-3-642-15561-1_11
URL : https://hal.archives-ouvertes.fr/inria-00548630

H. Jégou, F. Perronnin, M. Douze, J. Sánchez, P. Pérez et al., Aggregating Local Image Descriptors into Compact Codes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.9, 2011.
DOI : 10.1109/TPAMI.2011.235

D. Picard and P. Gosselin, Improving image similarity with vectors of locally aggregated tensors, 2011 18th IEEE International Conference on Image Processing, 2011.
DOI : 10.1109/ICIP.2011.6116641
URL : https://hal.archives-ouvertes.fr/hal-00591993

M. Everingham, L. Van-gool, C. K. Williams, J. Winn, and A. Zisserman, The Pascal Visual Object Classes (VOC) Challenge, International Journal of Computer Vision, vol.73, issue.2, 2007.
DOI : 10.1371/journal.pcbi.0040027

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. Wang, A. Klaser, C. Schmid, and C. Liu, Action recognition by dense trajectories, CVPR 2011, pp.3169-3176, 2011.
DOI : 10.1109/CVPR.2011.5995407
URL : https://hal.archives-ouvertes.fr/inria-00583818

I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld, Learning realistic human actions from movies, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587756
URL : https://hal.archives-ouvertes.fr/inria-00548659

C. Schuldt, I. Laptev, and B. Caputo, Recognizing human actions: a local SVM approach, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., pp.32-36, 2004.
DOI : 10.1109/ICPR.2004.1334462

H. Bay, T. Tuytelaars, and L. Van-gool, Surf: Speeded up robust features, ECCV, pp.404-417, 2006.
DOI : 10.1007/11744023_32

N. Dalal and B. , Triggs, Histograms of oriented gradients for human detection, Conference on CVPR, pp.886-893, 2005.

N. Dalal, B. Triggs, and C. Schmid, Human Detection Using Oriented Histograms of Flow and Appearance, ECCV, vol.38, issue.1, pp.428-441, 2006.
DOI : 10.1109/ICCV.2003.1238422
URL : https://hal.archives-ouvertes.fr/inria-00548587

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.10, pp.1615-1630, 2005.
DOI : 10.1109/TPAMI.2005.188
URL : https://hal.archives-ouvertes.fr/inria-00548227

E. Tola, V. Lepetit, and P. Fua, DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.5, pp.815-830, 2010.
DOI : 10.1109/TPAMI.2009.77

J. Davis and A. Bobick, The representation and recognition of action using temporal templates, Conference on CVPR, pp.928-934, 1997.

V. Kellokumpu, G. Zhao, and M. Pietikäinen, Texture Based Description of Movements for Activity Analysis, pp.206-213, 2008.

T. Ojala, M. Pietikäinen, and T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.7, pp.971-987, 2002.
DOI : 10.1109/TPAMI.2002.1017623
URL : http://www.ee.oulu.fi/research/imag/texture/publications/show_pdf.php?ID=94

V. Kellokumpu, G. Zhao, and M. Pietikäinen, Human activity recognition using a dynamic texture based method, pp.885-894, 2008.

L. Wang and D. Suter, Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition, IEEE Transactions on Image Processing, vol.16, issue.6, p.1646, 2007.
DOI : 10.1109/TIP.2007.896661

M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri, Actions as space-time shapes, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.1395-1402, 2005.
DOI : 10.1109/ICCV.2005.28

L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, Actions as Space-Time Shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.12, pp.2247-2253, 2007.
DOI : 10.1109/TPAMI.2007.70711

J. Liu, J. Luo, and M. Shah, Recognizing realistic actions from videos in the wild, Conference on CVPR, pp.1996-2003, 2009.

R. Polana and R. Nelson, Low level recognition of human motion, Proc. IEEE Workshop on Nonrigid and Articulate Motion, pp.77-82, 1994.

A. Efros, A. Berg, G. Mori, and J. Malik, Recognizing action at a distance, Proceedings Ninth IEEE International Conference on Computer Vision, pp.726-733, 2003.
DOI : 10.1109/ICCV.2003.1238420

B. D. Lucas and T. Kanade, An iterative image registration technique with an application to stereo vision, Proceedings of the 7th international joint conference on Artificial intelligence, pp.674-679, 1981.

A. Fathi and G. Mori, Action recognition by learning mid-level motion features, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587735
URL : http://www.cs.sfu.ca/~mori/research/papers/fathi_actionrecognition_cvpr08.pdf

S. Danafar and N. Gheissari, Action Recognition for Surveillance Applications Using Optic Flow and SVM, pp.457-466, 2007.
DOI : 10.1007/978-3-540-76390-1_45

D. Tran and A. Sorokin, Human Activity Recognition with Metric Learning, ECCV, vol.24, issue.4, pp.548-561, 2008.
DOI : 10.1109/CVPR.2007.383131

S. Ali and M. Shah, Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.2, pp.288-303, 2010.
DOI : 10.1109/TPAMI.2008.284

P. Dollár, V. Rabaud, G. Cottrell, and S. Belongie, Behavior recognition via Table 10: Mean Average Precision on the UCF11 dataset ; ND: number of descriptors ; NL: non-linear classifiers ; In [52] HOG/HOF descriptors are accumulated on over 100 spatio-temporal regions each one leading to a different BoW signature sparse spatio-temporal features, 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp.65-72, 2005.

A. Klaser, M. Marszalek, and C. Schmid, A Spatio-Temporal Descriptor Based on 3D-Gradients, Procedings of the British Machine Vision Conference 2008, 2008.
DOI : 10.5244/C.22.99
URL : https://hal.archives-ouvertes.fr/inria-00514853

P. Scovanner, S. Ali, and M. Shah, A 3-dimensional sift descriptor and its application to action recognition, Proceedings of the 15th international conference on Multimedia , MULTIMEDIA '07, pp.357-360, 2007.
DOI : 10.1145/1291233.1291311

G. Willems, T. Tuytelaars, and L. , Van Gool, An efficient dense and scaleinvariant spatio-temporal interest point detector, ECCV, pp.650-663, 2008.

O. Kihl, B. Tremblais, B. Augereau, and M. Khoudeir, Human activities discrimination with motion approximation in polynomial bases, 2010 IEEE International Conference on Image Processing, pp.2469-2472, 2010.
DOI : 10.1109/ICIP.2010.5651327
URL : https://hal.archives-ouvertes.fr/hal-00594762

V. F. Mota, E. Perez, M. B. Vieira, L. Maciel, F. Precioso et al., A Tensor Based on Optical Flow for Global Description of Motion in Videos, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, pp.2012-298
DOI : 10.1109/SIBGRAPI.2012.48
URL : https://hal.archives-ouvertes.fr/hal-00753159

O. Kihl, D. Picard, and P. Gosselin, Local polynomial space???time descriptors for action classification, Machine Vision and Applications, vol.2010, issue.6, 2013.
DOI : 10.1109/TIP.2007.896661
URL : https://hal.archives-ouvertes.fr/hal-01097536

J. Sánchez, F. Perronnin, and T. D. Campos, Modeling the spatial layout of images beyond spatial pyramids, Pattern Recognition Letters, vol.33, issue.16
DOI : 10.1016/j.patrec.2012.07.019

H. Wang, M. M. Ullah, A. Klaser, I. Laptev, and C. Schmid, Evaluation of local spatio-temporal features for action recognition, Procedings of the British Machine Vision Conference 2009, 2009.
DOI : 10.5244/C.23.124
URL : https://hal.archives-ouvertes.fr/inria-00439769

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.3360-3367, 2010.
DOI : 10.1109/CVPR.2010.5540018
URL : http://www.ifp.illinois.edu/%7Ejyang29/papers/CVPR10-LLC.pdf

J. Yang, K. Yu, Y. Gong, and T. Huang, Linear spatial pyramid matching using sparse coding for image classification, Conference on CVPR, pp.1794-1801, 2009.

S. Avila, N. Thome, M. Cord, E. Valle, A. De et al., BOSSA: Extended bow formalism for image classification, 2011 18th IEEE International Conference on Image Processing, pp.2909-2912, 2011.
DOI : 10.1109/ICIP.2011.6116268
URL : https://hal.archives-ouvertes.fr/hal-00625533

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, pp.3304-3311, 2010.
DOI : 10.1109/CVPR.2010.5540039

X. Zhou, K. Yu, T. Zhang, and T. Huang, Image classification using supervector coding of local image descriptors, 2010.

D. Picard and P. Gosselin, Efficient image signatures and similarities using tensor products of local descriptors, Computer Vision and Image Understanding, vol.117, issue.6, pp.680-687, 2013.
DOI : 10.1016/j.cviu.2013.02.004
URL : https://hal.archives-ouvertes.fr/hal-00799074

D. Picard and P. Gosselin, Improving image similarity with vectors of locally aggregated tensors, 2011 18th IEEE International Conference on Image Processing, pp.669-672, 2011.
DOI : 10.1109/ICIP.2011.6116641
URL : https://hal.archives-ouvertes.fr/hal-00591993

R. Negrel, D. Picard, and P. Gosselin, Using spatial pyramids with compacted vlat for image categorization, pp.2460-2463, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00753158

M. Varma and A. Zisserman, A Statistical Approach to Material Classification Using Image Patch Exemplars, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.11, pp.2032-2047, 2009.
DOI : 10.1109/TPAMI.2008.182

R. Negrel, D. Picard, and P. Gosselin, Compact tensor based image representation for similarity search, 2012 19th IEEE International Conference on Image Processing, pp.2425-2428, 2012.
DOI : 10.1109/ICIP.2012.6467387
URL : https://hal.archives-ouvertes.fr/hal-00753157

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.2169-2178, 2006.
DOI : 10.1109/CVPR.2006.68
URL : https://hal.archives-ouvertes.fr/inria-00548585

L. Li, H. Su, E. P. Xing, and L. Fei-fei, Object bank: A high-level image representation for scene classification and semantic feature sparsification, Advances in Neural Information Processing Systems 24

B. Horn and B. Schunck, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2
URL : http://www.liralab.it/teaching/SINA/papers/horn-schunck-81.pdf

A. Gilbert, J. Illingworth, and R. Bowden, Action Recognition Using Mined Hierarchical Compound Features, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.5, pp.883-897, 2011.
DOI : 10.1109/TPAMI.2010.144

M. Ullah, S. Parizi, and I. Laptev, Improving bag-of-features action recognition with non-local cues, Procedings of the British Machine Vision Conference 2010, 2010.
DOI : 10.5244/C.24.95

C. Farabet, C. Couprie, L. Najman, and Y. Lecun, Learning Hierarchical Features for Scene Labeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1915-1929, 2013.
DOI : 10.1109/TPAMI.2012.231
URL : https://hal.archives-ouvertes.fr/hal-00742077

A. Rakotomamonjy, R. Flamary, and F. Yger, Learning with infinitely many features, Machine Learning, vol.44, issue.7, pp.43-6610, 2013.
DOI : 10.1016/j.patcog.2011.03.006
URL : https://hal.archives-ouvertes.fr/hal-00880945

H. Hotelling, Analysis of a complex of statistical variables into principal components., Journal of Educational Psychology, vol.24, issue.6, p.417, 1933.
DOI : 10.1037/h0071325

A. Bertrand and M. Moonen, Distributed adaptive estimation of covariance matrix eigenvectors in wireless sensor networks with application to distributed PCA, Signal Processing, vol.104, pp.120-135, 2014.
DOI : 10.1016/j.sigpro.2014.03.037

J. Fellus, D. Picard, and P. Gosselin, Dimensionality reduction in decentralized networks by gossip aggregation of principal components analyzers, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.171-176, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00985721

D. Kempe and F. Mcsherry, A decentralized algorithm for spectral analysis, Journal of Computer and System Sciences, vol.74, issue.1, pp.70-83, 2008.
DOI : 10.1016/j.jcss.2007.04.014

A. Wiesel and A. O. Hero, Decomposable Principal Component Analysis, IEEE Transactions on Signal Processing, vol.57, issue.11, pp.4369-4377, 2009.
DOI : 10.1109/TSP.2009.2025806
URL : http://arxiv.org/pdf/0808.2337

Z. Meng, A. Wiesel, and A. O. Hero, Distributed principal component analysis on networks via directed graphical models, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.2877-2880, 2012.
DOI : 10.1109/ICASSP.2012.6288518

C. Ordonez, N. Mohanam, and C. Garcia-alvarado, Pca for large data sets with parallel data summarization, Distributed and Parallel Databases, pp.377-403, 2014.

P. Bruneau, M. Gelgon, and F. Picarougne, Aggregation of Probabilistic PCA Mixtures with a Variational-Bayes Technique Over Parameters, 2010 20th International Conference on Pattern Recognition, pp.702-705, 2010.
DOI : 10.1109/ICPR.2010.177
URL : https://hal.archives-ouvertes.fr/hal-00471313

M. E. Tipping and C. M. Bishop, Mixtures of Probabilistic Principal Component Analyzers, Neural Computation, vol.2, issue.1, pp.443-482, 1999.
DOI : 10.1007/BF00162527

A. Nikseresht and M. Gelgon, Gossip-Based Computation of a Gaussian Mixture Model for Distributed Multimedia Indexing, IEEE Transactions on Multimedia, vol.10, issue.3, pp.385-392, 2008.
DOI : 10.1109/TMM.2008.917343
URL : https://hal.archives-ouvertes.fr/inria-00368854

A. Nikseresht, Estimation de modèles de mélange probabilistes: une proposition pour un fonctionnement réparti et décentralisé, 2008.

S. V. Macua, P. Belanovic, and S. Zazo, Consensus-based distributed principal component analysis in wireless sensor networks, Signal Processing Advances in Wireless Communications (SPAWC), pp.1-5, 2010.

D. Kempe, A. Dobra, and J. Gehrke, Gossip-based computation of aggregate information, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings., p.482, 2003.
DOI : 10.1109/SFCS.2003.1238221

F. Iutzeler, P. Ciblat, and W. Hachem, Analysis of Sum-Weight-Like Algorithms for Averaging in Wireless Sensor Networks, IEEE Transactions on Signal Processing, vol.61, issue.11
DOI : 10.1109/TSP.2013.2256904
URL : https://hal.archives-ouvertes.fr/hal-00737584

M. Jelasity, W. Kowalczyk, and M. Van-steen, Newscast computing, Tech. rep, 2003.

D. Shah, Gossip Algorithms, Foundations and Trends?? in Networking, vol.3, issue.1, 2009.
DOI : 10.1561/1300000014

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998.
DOI : 10.1109/5.726791

A. Rhodius, On the maximum of ergodicity coefficients, the Dobrushin ergodicity coefficient, and products of stochastic matrices, Linear Algebra and its Applications, vol.253, issue.1-3, pp.141-154, 1997.
DOI : 10.1016/0024-3795(95)00706-7

E. Seneta, Non-negative matrices and Markov chains, 2006.
DOI : 10.1007/0-387-32792-4

D. Carbonera, L. Hedi, T. David, and P. , Learning features combination for human action recognition from skeleton sequences, Pattern Recognition Letters, vol.99, p.31, 2017.
DOI : 10.1016/j.patrec.2017.02.001
URL : https://hal.archives-ouvertes.fr/hal-01515376

S. Mark, L. Nicolas, . Roux, and B. Francis, Minimizing finite sums with the stochastic average gradient, In : Mathematical Programming, vol.1621, issue.2, pp.83-112, 2017.

A. Relja, NetVLAD : CNN architecture for weakly supervised place recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.5297-5307, 2016.

C. Micael, Deep neural networks under stress, Image Processing (ICIP), 2016 IEEE International Conference on. IEEE. 2016, pp.4443-4447

H. Gao, Deep networks with stochastic depth, European Conference on Computer Vision, pp.646-661, 2016.

K. Olivier, P. David, and P. Gosselin, Local polynomial space?time descriptors for action classification, Machine Vision and Applications, pp.351-361, 2016.

L. Meng and L. Howard, Graph-based approach for 3D human skeletal action recognition, Pattern Recognition Letters, pp.167-8655, 2016.

P. David, Preserving local spatial information in image similarity using tensor aggregation of local features, Image Processing (ICIP), 2016 IEEE International Conference on. IEEE. 2016, pp.201-205

P. David, H. Thomas, and D. Georg, Non-negative dictionary learning for paper watermark similarity, Signals, Systems and Computers, 2016 50th Asilomar Conference on. IEEE. 2016, pp.130-133

H. Rahmani, A. Mahmood, D. Huynh, and A. Mian, Histogram of Oriented Principal Components for Cross-View Action Recognition, TPAMI) PP.99 (2016), pp.1-1
DOI : 10.1109/TPAMI.2016.2533389

D. Thibaut, T. Nicolas, and C. Matthieu, MANTRA : Minimum Maximum Latent Structural SVM for Image Classification and Ranking, Proceedings of the IEEE International Conference on Computer Vision. 2015, pp.2713-2721

F. Jerome, P. David, and P. Gosselin, Asynchronous gossip principal components analysis, Neurocomputing, vol.169, pp.262-271, 2015.

F. Jérôme, P. David, and P. Gosselin, Indexation multimédia par dictionnaires visuels en environnement décentralisé. Une approche par protocoles Gossip, In : Traitement du Signal, vol.321, pp.39-64, 2015.

I. Yani, Training cnns with low-rank filters for efficient image classification) (cf, p.49

K. Olivier, P. David, and P. Gosselin, A unified framework for local visual descriptors evaluation, Pattern Recognition, vol.484, issue.28, pp.1174-1184, 2015.

P. David, P. Gosselin, and G. Marie-claude, Challenges in Content-Based Image Indexing of Cultural Heritage Collections, Signal Processing Magazine, pp.95-102, 2015.

V. Vivek, Z. Naifan, and Q. Guo-jun, Differential Recurrent Neural Networks for Action Recognition, IEEE International Conference on Computer Vision (ICCV)

Z. Sixin, E. Anna, . Choromanska, and L. Yann, Deep learning with elastic averaging SGD, Advances in Neural Information Processing Systems. 2015, pp.685-693

D. Maxime, 3D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold, IEEE Transactions on Cybernetics

F. Jerome, P. David, and P. Gosselin, Dimensionality reduction in decentralized networks by Gossip aggregation of principal components analyzers, pp.171-176

C. Lu, J. Jia, and C. K. Tang, Range-Sample Depth Feature for Action Recognition, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.772-779
DOI : 10.1109/CVPR.2014.104

N. Romain, P. David, and P. Gosselin, Efficient Metric Learning Based Dimension Reduction Using Sparse Projectors For Image Near Duplicate Retrieval, pp.2014-2040

P. David and F. Inbar, Second order model deviations of local Gabor features for texture classification, Signals, Systems and Computers, 2014 48th Asilomar Conference on. IEEE. 2014, pp.917-920

P. David, V. Ngoc-son, and F. Inbar, Photographic paper texture classification using model deviation of local visual descriptors, IEEE Int. Conf. on Image Processing. 2014, pp.5701-5705

S. Karen and Z. Andrew, Very deep convolutional networks for largescale image recognition) (cf, p.49

S. Nitish, Dropout : A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research, vol.15, pp.1929-1958, 2014.

R. Vemulapalli, F. Arrate, and R. Chellappa, Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.588-595
DOI : 10.1109/CVPR.2014.82

X. Yang and Y. Tian, Super Normal Vector for Activity Recognition Using Depth Sequences, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.804-811
DOI : 10.1109/CVPR.2014.108

F. Jerome, P. David, and P. Gosselin, Decentralized K-means using randomized Gossip protocols for clustering large datasets, IEEE 13th International Conference on Data Mining Workshops. IEEE. 2013, pp.599-606

G. Ian, Maxout Networks, International Conference on Machine Learning. 2013, pp.1319-1327

L. Jiajia, W. Wei, and Q. Hairong, Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps, IEEE International Conference on Computer Vision (ICCV). 2013, pp.1809-1816

N. Romain, P. David, and P. Gosselin, Web scale image retrieval using compact tensor aggregation of visual descriptors, IEEE Multimedia, vol.203, issue.24, pp.24-33, 2013.

O. Oreifej and L. Zicheng, HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.716-723
DOI : 10.1109/CVPR.2013.98

P. David and P. Gosselin, Efficient image signatures and similarities using tensor products of local descriptors, In : Computer Vision and Image Understanding, vol.1176, issue.48, pp.680-687, 2013.

P. David, T. Nicolas, and C. Matthieu, JKernelMachines : A simple framework for Kernel Machines, In : Journal of Machine Learning Research, vol.14, pp.1417-1421, 2013.

L. Seidenari, Recognizing Actions from Depth Cameras as Weakly Aligned Multipart Bag-of-Poses, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2013, pp.479-485

Q. D. Tran and N. Q. Ly, An effective fusion scheme of spatio-temporal features for human action recognition in RGB-D video, 2013 International Conference on Control, Automation and Information Sciences (ICCAIS), pp.246-251
DOI : 10.1109/ICCAIS.2013.6720562

J. Hervé and P. Florent, Aggregating local image descriptors into compact codes, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.1, 2012.

N. Romain, P. David, and P. Gosselin, Compact Tensor Based Image Representation for Similarity Search, International Conference on Image Processing. 2012 (cf, p.22

V. Ngoc-son and C. Alice, Enhanced Patterns of Oriented Edge Magnitudes for Face Recognition and Image Matching, Image Processing, pp.1352-1365, 2012.
DOI : 10.1109/TIP.2011.2166974

W. Jiang, L. Zicheng, W. Ying, and Y. Junsong, Mining actionlet ensemble for action recognition with depth cameras, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.1290-1297
DOI : 10.1109/CVPR.2012.6247813

X. Lu, C. Chia-chih, and J. K. Aggarwal, View Invariant Human Action Recognition Using Histograms of 3D Joints, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, pp.20-27, 2012.

P. David and P. Gosselin, Improving Image Similarity With Vectors of Locally Aggregated Tensors, Image Processing (ICIP) 18th IEEE International Conference on, pp.669-691, 2011.

W. Heng, K. Alexander, C. Schmid, and C. Liu, Action Recognition by Dense Trajectories, IEEE International Conference on Computer Vision and Pattern Recognition, 2011.

B. Florence, Weighted gossip : Distributed averaging using non-doubly stochastic matrices, 2010 ieee international symposium on. IEEE. 2010, pp.1753-1757

J. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online Learning for Matrix Factorization and Sparse Coding, International Journal on Machine Learning Research, vol.11, pp.19-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00408716

F. Perronnin, Y. Liu, J. Sanchez, and H. Poirier, Large-scale image retrieval with compressed Fisher vectors, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOI : 10.1109/CVPR.2010.5540009

M. M. Ullah, S. Parizi, and I. Laptev, Improving bag-of-features action recognition with non-local cues, Procedings of the British Machine Vision Conference 2010, p.2010
DOI : 10.5244/C.24.95

K. Q. Weinberger and L. K. Saul, Distance Metric Learning for Large Margin Nearest Neighbor Classification, The Journal of Machine Learning Research (JMLR), vol.10, pp.207-244, 2009.

H. Bay, A. Ess, T. Tuytelaars, L. Van, and G. , SURF : Speeded Up Robust Features, Computer Vision and Image Understanding, vol.1103, pp.346-359, 2008.
DOI : 10.1007/11744023_32

J. Hervé, D. Matthijs, C. Schmid-'andrew-zisserman, D. Forsyth, and P. Torr, Hamming embedding and weak geometric consistency for large scale image search, European Conference on Computer Vision Tome I. LNCS. Springer, oct, pp.304-317, 2008.

N. Dalal, B. Triggs, and C. Schmid, Human Detection Using Oriented Histograms of Flow and Appearance, pp.428-441, 2006.
DOI : 10.1109/ICCV.2003.1238422
URL : https://hal.archives-ouvertes.fr/inria-00548587

L. Svetlana, C. Schmid, and P. Jean, Beyond Bags of Features : Spatial Pyramid Matching for Recognizing Natural Scene Categories, IEEE International Conference on Computer Vision and Pattern Recognition, pp.2169-2178, 2006.

D. Navneet, T. Bill, S. Cordelia, S. Stefano, and T. Carlo, Histograms of Oriented Gradients for Human Detection, IEEE International Conference on Computer Vision and Pattern Recognition. Sous la direction de, pp.886-893, 2005.

D. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision, vol.60, issue.2, pp.60-91, 2004.
DOI : 10.1023/B:VISI.0000029664.99615.94
URL : http://www.cs.ubc.ca/~lowe/papers/ijcv03.ps

J. Shawe-taylor and N. Cristianini, Kernel methods for Pattern Analysis, 2004.
DOI : 10.1017/CBO9780511809682

A. A. Efros, A. C. Berg, G. Mori, and J. Malik, Recognizing action at a distance, Proceedings Ninth IEEE International Conference on Computer Vision, pp.726-733, 2003.
DOI : 10.1109/ICCV.2003.1238420

S. Kenji, H. Isao, and S. Noboru, A simple neural network pruning algorithm with application to filter synthesis, Neural Processing Letters 13, pp.43-53, 2001.

S. Tong and D. Koller, Support vector machine active learning with application to text classification, International Journal on Machine Learning Research, vol.2, pp.45-66, 2001.

F. Marguerite and W. Philip, An algorithm for quadratic programming, In : Naval Research Logistics Quarterly, vol.3, pp.1-2, 1956.