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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.4931

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.

H. Jegou, M. Douze, and C. Schmid, Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search, European Conference on Computer Vision, pp.304-317, 2008.
DOI : 10.1007/978-3-540-88682-2_24
URL : https://hal.archives-ouvertes.fr/inria-00316866

H. Jégou, M. Douze, and C. Schmid, Product Quantization for Nearest Neighbor Search, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.1, pp.117-128, 2011.
DOI : 10.1109/TPAMI.2010.57

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

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

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, pp.3304-3311, 2012.
DOI : 10.1109/TPAMI.2011.235

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, 2010.
DOI : 10.1109/CVPR.2010.5540009

R. Negrel, D. Picard, and P. Gosselin, Dimensionality reduction of visual features using sparse projectors for content-based image retrieval, 2014 IEEE International Conference on Image Processing (ICIP), pp.2192-2196, 2014.
DOI : 10.1109/ICIP.2014.7025444
URL : https://hal.archives-ouvertes.fr/hal-01081770

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

P. Koniusz and K. Mikolajczyk, Spatial Coordinate Coding to reduce histogram representations, Dominant Angle and Colour Pyramid Match, 2011 18th IEEE International Conference on Image Processing, 2011.
DOI : 10.1109/ICIP.2011.6116639
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.377.1805

E. George, . Dahl, N. Tara, G. E. Sainath, and . Hinton, Improving deep neural networks for lvcsr using rectified linear units and dropout, Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pp.8609-8613, 2013.

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2016.90
URL : http://arxiv.org/abs/1512.03385

D. Erhan, Y. Bengio, A. Courville, P. Manzagol, P. Vincent et al., Why does unsupervised pre-training help deep learning?, The Journal of Machine Learning Research, vol.11, pp.625-660, 2010.

J. Bruna and S. Mallat, Invariant scattering convolution networks Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.35, issue.8, pp.1872-1886, 2013.

P. F. Felzenszwalb, R. B. Girshick, D. Mcallester, and D. Ramanan, Object Detection with Discriminatively Trained Part-Based Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1627-1645, 2010.
DOI : 10.1109/TPAMI.2009.167

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

P. Gosselin, N. Murray, H. Jégou, and F. Perronnin, Revisiting the Fisher vector for fine-grained classification, Pattern Recognition Letters, vol.49, pp.92-98, 2014.
DOI : 10.1016/j.patrec.2014.06.011
URL : https://hal.archives-ouvertes.fr/hal-01056223

A. Vedaldi and B. Fulkerson, Vlfeat, Proceedings of the international conference on Multimedia, MM '10, 2008.
DOI : 10.1145/1873951.1874249

T. Do, D. Quang, N. Tran, and . Cheung, Faemb: a function approximation-based embedding method for image retrieval, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3556-3564, 2015.

R. Arandjelovic and A. Zisserman, All About VLAD, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.1578-1585, 2013.
DOI : 10.1109/CVPR.2013.207

R. Negrel, D. Picard, and P. Gosselin, Web-Scale Image Retrieval Using Compact Tensor Aggregation of Visual Descriptors, IEEE MultiMedia, vol.20, issue.3, pp.24-33, 2013.
DOI : 10.1109/MMUL.2013.14
URL : https://hal.archives-ouvertes.fr/hal-00832760