S. Agarwal, Y. Furukawa, N. Snavely, I. Simon, B. Curless et al., Building Rome in a day, Communications of the ACM, issue.2, 2011.

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

A. Babenko, A. Slesarev, A. Chigorin, and V. Lempitsky, Neural Codes for Image Retrieval, ECCV, 2008.
DOI : 10.1007/978-3-319-10590-1_38

L. Bo, K. Lai, X. Ren, and D. Fox, Object recognition with hierarchical kernel descriptors, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995719

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.225.931

L. Bo, X. Ren, and D. Fox, Kernel descriptors for visual recognition, NIPS, p.5, 2010.

L. Bottou, Stochastic Gradient Descent Tricks, Neural Networks: Tricks of the Trade, 2012.
DOI : 10.1137/1116025

M. Calonder, V. Lepetit, C. Strecha, and P. Fua, BRIEF: Binary Robust Independent Elementary Features, ECCV, 2010.
DOI : 10.1007/978-3-642-15561-1_56

J. Deng, W. Dong, R. Socher, L. Li, K. Li et al., ImageNet: A large-scale hierarchical image database, CVPR, 2009.

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang et al., Decaf: A deep convolutional activation feature for generic visual recognition, ICML, 2014.

P. Fischer, A. Dosovitskiy, and T. Brox, Descriptor matching with convolutional neural networks: a comparison to SIFT. arXiv Preprint, 2007.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.81

URL : http://arxiv.org/abs/1311.2524

Y. Gong, L. Wang, R. Guo, and S. Lazebnik, Multi-scale Orderless Pooling of Deep Convolutional Activation Features, ECCV, p.8, 2014.
DOI : 10.1007/978-3-319-10584-0_26

H. Jégou and O. Chum, Negative evidences and cooccurrences in image retrieval: the benefit of PCA and whitening, ECCV, 2012.

H. Jegou, M. Douze, and C. Schmid, Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search, ECCV, 2008.
DOI : 10.1007/978-3-540-88682-2_24

URL : https://hal.archives-ouvertes.fr/inria-00316866

H. Jegou, M. Douze, and C. Schmid, Product Quantization for Nearest Neighbor Search, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.1, 2008.
DOI : 10.1109/TPAMI.2010.57

URL : https://hal.archives-ouvertes.fr/inria-00514462

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, 2010.
DOI : 10.1109/CVPR.2010.5540039

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, 2012.
DOI : 10.1109/TPAMI.2011.235

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, 2014.
DOI : 10.1145/2647868.2654889

J. Jiang, Y. Song, T. Leung, C. Rosenberg, J. Wang et al., Learning fine-grained image similarity with deep ranking, CVPR, 2014.

A. Krizhevsky, I. Sutskever, and G. Hinton, ImageNet classification with deep convolutional neural networks, 2004.

Y. Lecun, B. Boser, J. Denker, D. Henderson, R. Howard et al., Handwritten digit recognition with a back-propagation network, NIPS, issue.1, 1989.

Y. Li, N. Snavely, and D. P. Huttenlocher, Location Recognition Using Prioritized Feature Matching, ECCV, 2010.
DOI : 10.1007/978-3-642-15552-9_57

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.5605

D. G. Lowe, Distinctive image features from scale-invariant keypoints. IJCV, 2003.

K. Mikolajczyk and C. Schmid, Scale & Affine Invariant Interest Point Detectors, International Journal of Computer Vision, vol.60, issue.1, 2004.
DOI : 10.1023/B:VISI.0000027790.02288.f2

URL : https://hal.archives-ouvertes.fr/inria-00548554

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, p.5, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548227

K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas et al., A Comparison of Affine Region Detectors, International Journal of Computer Vision, vol.65, issue.1-2, 2005.
DOI : 10.1007/s11263-005-3848-x

URL : https://hal.archives-ouvertes.fr/inria-00548528

M. Oquab, L. Bottou, I. Laptev, and J. Sivic, Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2002.
DOI : 10.1109/CVPR.2014.222

URL : https://hal.archives-ouvertes.fr/hal-00911179

M. Perdoch, O. Chum, and J. Matas, Efficient representation of local geometry for large scale object retrieval, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206529

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

F. Perronnin, J. Sánchez, and Y. Liu, Large-scale image categorization with explicit data embedding, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004.
DOI : 10.1109/CVPR.2010.5539914

J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, Object retrieval with large vocabularies and fast spatial matching, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383172

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, 2008.
DOI : 10.1109/CVPR.2008.4587635

J. Philbin, M. Isard, J. Sivic, and A. Zisserman, Descriptor Learning for Efficient Retrieval, ECCV, 2010.
DOI : 10.1007/978-3-642-15558-1_49

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.6458

A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, CNN features off-the-shelf: an astounding baseline for recognition. arXiv Preprint, 2014.

E. Simo-serra, E. Trulls, L. Ferraz, I. Kokkinos, and F. Moreno-noguer, Fracking deep convolutional image descriptors . Arxiv preprint, 2015.
DOI : 10.1109/iccv.2015.22

URL : http://hdl.handle.net/10261/133257

K. Simonyan, A. Vedaldi, and A. Zisserman, Learning Local Feature Descriptors Using Convex Optimisation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.8, 2014.
DOI : 10.1109/TPAMI.2014.2301163

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

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, 2010.
DOI : 10.1109/TPAMI.2009.77

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.168.5084

T. Tuytelaars and K. Mikolajczyk, Local invariant feature detectors: A survey. Foundations and Trends in Computer Graphics and Vision, 2008.

A. Vedaldi and A. Zisserman, Efficient additive kernels via explicit feature maps. TPAMI, 2004.
DOI : 10.1109/cvpr.2010.5539949

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.167.7024

Z. Wang, B. Fan, and F. Wu, Local intensity order pattern for feature description, ICCV, 2011.

S. Winder, G. Hua, and M. Brown, Picking the best DAISY, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2006.
DOI : 10.1109/CVPR.2009.5206839