]. G. Schindler, M. Brown, R. Szeliski, J. Knopp, J. Sivic et al., City-scale location recognition Avoiding confusing features in place recognition City-scale landmark identification on mobile devices Fast image-based localization using direct 2D?to?3D matching Image retrieval for image-based localization revisited Learning and calibrating per-location classifiers for visual place recognition Graph-based discriminative learning for location recognition Visual place recognition with repetitive structures DisLocation: Scalable descriptor distinctiveness for location recognition, Proc. CVPR Proc. ECCV Proc. CVPR Proc. ICCV Proc. BMVC. Proc. CVPR Proc. CVPR, 2013. [9] R. Arandjelovi´cArandjelovi´c and A. Zisserman Proc. ACCV, pp.319-336, 2007.

A. Torii, R. Arandjelovi´carandjelovi´c, J. Sivic, M. Okutomi, and T. Pajdla, 24/7 place recognition by view synthesis, Proc. CVPR, 2015.
DOI : 10.1109/tpami.2017.2667665

URL : https://hal.archives-ouvertes.fr/hal-01147212

T. Sattler, M. Havlena, F. Radenovi´cradenovi´c, K. Schindler, and M. Pollefeys, Hyperpoints and Fine Vocabularies for Large-Scale Location Recognition, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.243

M. Cummins and P. Newman, FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance, The International Journal of Robotics Research, vol.27, issue.6, 2008.
DOI : 10.1177/0278364908090961

C. Mcmanus, W. Churchill, W. Maddern, A. Stewart, and P. Newman, Shady dealings: Robust, long-term visual localisation using illumination invariance, 2014 IEEE International Conference on Robotics and Automation (ICRA), 2014.
DOI : 10.1109/ICRA.2014.6906961

W. Maddern and S. Vidas, Towards robust night and day place recognition using visible and thermal imaging, Proc. Intl. Conf. on Robotics and Automation, 2014.

N. Sunderhauf, S. Shirazi, A. Jacobson, E. Pepperell, F. Dayoub et al., Place Recognition with ConvNet Landmarks: Viewpoint-Robust, Condition-Robust, Training-Free, Robotics: Science and Systems XI, 2015.
DOI : 10.15607/RSS.2015.XI.022

S. Middelberg, T. Sattler, O. Untzelmann, and L. Kobbelt, Scalable 6-DOF Localization on Mobile Devices, Proc. ECCV, 2014.
DOI : 10.1007/978-3-319-10605-2_18

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.667.8896

M. Aubry, B. C. Russell, and J. Sivic, Painting-to-3D model alignment via discriminative visual elements, ACM Transactions on Graphics, vol.33, issue.2, p.14, 2014.
DOI : 10.1561/0600000009

URL : https://hal.archives-ouvertes.fr/hal-00863615

T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends?? in Computer Graphics and Vision, vol.3, issue.3, pp.177-280, 2008.
DOI : 10.1561/0600000017

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.377.3635

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

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

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

R. Arandjelovi´carandjelovi´c and A. Zisserman, All about VLAD, Proc. CVPR, 2013.

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

F. Perronnin, Y. Liu, J. Sánchez, 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

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

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, 2011.
DOI : 10.1109/TPAMI.2010.57

Y. Li, N. Snavely, D. Huttenlocher, and P. Fua, Worldwide pose estimation using 3D point clouds, Proc. ECCV, 2012.
DOI : 10.1007/978-3-642-33718-5_2

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.362.4910

Y. Lecun, B. Boser, J. S. Denker, D. Henderson, R. E. 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, vol.86, issue.11, pp.2278-2324, 1998.
DOI : 10.1109/5.726791

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.1115

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, NIPS, pp.1106-1114, 2012.
DOI : 10.1162/neco.2009.10-08-881

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.299.205

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, 2014.
DOI : 10.1109/CVPR.2014.222

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

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, Proc. ICLR, 2015.

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
DOI : 10.1109/CVPR.2015.7298594

URL : http://arxiv.org/abs/1409.4842

B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, Learning deep features for scene recognition using places database, NIPS, 2014.
DOI : 10.1109/tpami.2017.2723009

R. B. 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

J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang et al., DeCAF: A deep convolutional activation feature for generic visual recognition, 1310.

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus et al., OverFeat: Integrated recognition, localization and detection using convolutional networks, 1312.

M. D. Zeiler and R. Fergus, Visualizing and Understanding Convolutional Networks, Proc. ECCV, 2014.
DOI : 10.1007/978-3-319-10590-1_53

URL : http://arxiv.org/abs/1311.2901

H. Azizpour, A. Razavian, J. Sullivan, A. Maki, and S. Carlsson, Factors of transferability from a generic ConvNet representation, 1406.

A. Babenko and V. Lempitsky, Aggregating local deep features for image retrieval, Proc. ICCV, 2015.

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

URL : http://arxiv.org/abs/1403.1840

A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, CNN Features Off-the-Shelf: An Astounding Baseline for Recognition, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 1403.
DOI : 10.1109/CVPRW.2014.131

URL : http://arxiv.org/abs/1403.6382

A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson, A baseline for visual instance retrieval with deep convolutional networks, Proc. ICLR, 2015.
DOI : 10.3169/mta.4.251

G. Tolias, R. Sicre, and H. Jégou, Particular object retrieval with integral max-pooling of CNN activations, Proc. ICLR, 2016.

Y. Kalantidis, C. Mellina, and S. Osindero, Cross-Dimensional Weighting for Aggregated Deep Convolutional Features, ECCV workshop on Web-scale Vision and Social Media, 2016.
DOI : 10.1016/j.cviu.2013.12.002

URL : http://arxiv.org/abs/1512.04065

E. Mohedano, K. Mcguinness, N. E. O-'connor, A. Salvador, F. Marqués et al., Bags of Local Convolutional Features for Scalable Instance Search, Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, ICMR '16, 2016.
DOI : 10.1145/2324796.2324856

R. Arandjelovi´carandjelovi´c and A. Zisserman, Three things everyone should know to improve object retrieval, Proc. CVPR, 2012.

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

O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman, Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval, 2007 IEEE 11th International Conference on Computer Vision, 2007.
DOI : 10.1109/ICCV.2007.4408891

O. Chum, A. Mikulik, M. Pe?, and J. Matas, Total recall II: Query expansion revisited, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995601

J. Delhumeau, P. Gosselin, H. Jégou, and P. Pérez, Revisiting the VLAD image representation, Proceedings of the 21st ACM international conference on Multimedia, MM '13, 2013.
DOI : 10.1145/2502081.2502171

URL : https://hal.archives-ouvertes.fr/hal-00840653

H. Jégou, M. Douze, and C. Schmid, Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search, Proc. ECCV, pp.304-317, 2008.
DOI : 10.1007/978-3-540-88682-2_24

H. Jégou, A. Zisserman, O. Chum, and J. Matas, Triangulation embedding and democratic aggregation for image search Learning a fine vocabulary, Proc. CVPR Proc. ECCV, 2010.

F. Perronnin and D. Dance, Fisher kernels on visual vocabularies for image categorization Lost in quantization: Improving particular object retrieval in large scale image databases, Proc. CVPR Proc. CVPR, 2007.

K. Simonyan, A. Vedaldi, and A. Zisserman, Descriptor Learning Using Convex Optimisation, Proc. ECCV, 2012.
DOI : 10.1007/978-3-642-33718-5_18

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.370.9634

G. Tolias, Y. Avrithis, and H. Jégou, To Aggregate or Not to aggregate: Selective Match Kernels for Image Search, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.177

URL : https://hal.archives-ouvertes.fr/hal-00864684

G. Tolias and H. Jégou, Visual query expansion with or without geometry: Refining local descriptors by feature aggregation, Pattern Recognition, vol.47, issue.10, 2014.
DOI : 10.1016/j.patcog.2014.04.007

URL : https://hal.archives-ouvertes.fr/hal-00971267

T. Turcot and D. G. Lowe, Better matching with fewer features: The selection of useful features in large database recognition problems, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009.
DOI : 10.1109/ICCVW.2009.5457541

H. Jégou, H. Harzallah, and C. Schmid, A contextual dissimilarity measure for accurate and efficient image search, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.382970

D. Qin, S. Gammeter, L. Bossard, T. Quack, and L. Van-gool, Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995373

D. Qin, C. Wengert, and L. V. , Query Adaptive Similarity for Large Scale Object Retrieval, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.211

J. Zepeda and P. Pérez, Exemplar SVMs as visual feature encoders, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298924

D. Qin, Y. Chen, M. Guillaumin, and L. V. , Learning to rank bagof-word histograms for large-scale object retrieval, Proc. BMVC, 2014.
DOI : 10.5244/c.28.43

S. Winder, G. Hua, and M. Brown, Picking the best DAISY, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.178-185, 2009.
DOI : 10.1109/CVPR.2009.5206839

J. Philbin, M. Isard, J. Sivic, and A. Zisserman, Descriptor Learning for Efficient Retrieval, Proc. 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. Makadia, Feature Tracking for Wide-Baseline Image Retrieval, Proc. ECCV, 2010.
DOI : 10.1007/978-3-642-15555-0_23

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.179.1610

A. Bergamo, S. N. Sinha, and L. Torresani, Leveraging Structure from Motion to Learn Discriminative Codebooks for Scalable Landmark Classification, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.104

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.367.5102

D. Qin, X. Chen, M. Guillaumin, and L. V. , Quantized kernel learning for feature matching, NIPS, 2014.

E. Simo-serra, E. Trulls, L. Ferraz, I. Kokkinos, and F. Moreno-noguer, Fracking deep convolutional image descriptors, 1412.
DOI : 10.1109/iccv.2015.22

URL : http://upcommons.upc.edu/bitstream/2117/84259/1/1694-Discriminative-Learning-of-Deep-Convolutional-Feature-Point-Descriptors%281%29.pdf

M. Paulin, M. Douze, Z. Harchaoui, J. Mairal, F. Perronnin et al., Local Convolutional Features with Unsupervised Training for Image Retrieval, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.19

URL : https://hal.archives-ouvertes.fr/hal-01207966

K. M. Yi, E. Trulls, V. Lepetit, and P. Fua, LIFT: Learned Invariant Feature Transform, Proc. ECCV, 2016.
DOI : 10.1109/TPAMI.2009.167

URL : http://arxiv.org/abs/1603.09114

A. Babenko, A. Slesarev, A. Chigorin, and V. Lempitsky, Neural Codes for Image Retrieval, Proc. ECCV, 2014.
DOI : 10.1007/978-3-319-10590-1_38

URL : http://arxiv.org/abs/1404.1777

A. Gordo, J. Almazán, J. Revaud, and D. Larlus, Deep Image Retrieval: Learning Global Representations for Image Search, Proc. ECCV, 2016.
DOI : 10.1109/CVPR.2014.180

URL : http://arxiv.org/abs/1604.01325

F. Radenovi´cradenovi´c, G. Tolias, and O. Chum, CNN image retrieval learns from BoW: Unsupervised fine-tuning with hard examples, Proc. ECCV, 2016.

R. Arandjelovi´carandjelovi´c, P. Gronat, A. Torii, T. Pajdla, and J. Sivic, NetVLAD: CNN architecture for weakly supervised place recognition, Proc. CVPR, 2016.

T. Weyand, I. Kostrikov, and J. Philbin, PlaNet - Photo Geolocation with Convolutional Neural Networks, Proc. ECCV, 2016.
DOI : 10.1145/1631272.1631468

URL : http://arxiv.org/pdf/1602.05314

A. Kendall, M. Grimes, and R. Cipolla, PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.336

URL : http://arxiv.org/abs/1505.07427

T. Lin, Y. Cui, S. Belongie, and J. Hays, Learning deep representations for ground-to-aerial geolocalization, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7299135

M. Cimpoi, S. Maji, and A. Vedaldi, Deep filter banks for texture recognition and segmentation, Proc. CVPR, 2015.
DOI : 10.1109/cvpr.2015.7299007

URL : https://hal.archives-ouvertes.fr/hal-01263622

G. Csurka, C. Bray, C. Dance, and L. Fan, Visual categorization with bags of keypoints, Workshop on Statistical Learning in Computer Vision, ECCV, pp.1-22, 2004.

V. Sydorov, M. Sakurada, and C. Lampert, Deep Fisher Kernels -- End to End Learning of the Fisher Kernel GMM Parameters, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.182

K. Simonyan, A. Vedaldi, and A. Zisserman, Deep Fisher networks for large-scale image classification, NIPS, 2013.

T. Lin, A. Roychowdhury, and S. Maji, Bilinear CNN Models for Fine-Grained Visual Recognition, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.170

URL : http://arxiv.org/abs/1504.07889

M. Schultz and T. Joachims, Learning a distance metric from relative comparisons, NIPS, 2004.

K. Q. Weinberger, J. Blitzer, and L. Saul, Distance metric learning for large margin nearest neighbor classification, NIPS, 2006.

J. Wang, Y. Song, T. Leung, C. Rosenberg, J. Wang et al., Learning Fine-Grained Image Similarity with Deep Ranking, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.180

URL : http://arxiv.org/abs/1404.4661

F. Schroff, D. Kalenichenko, and J. Philbin, FaceNet: A unified embedding for face recognition and clustering, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298682

URL : http://arxiv.org/abs/1503.03832

J. Foulds and E. Frank, A review of multi-instance learning assumptions, The Knowledge Engineering Review, vol.2, issue.01, pp.1-25, 2010.
DOI : 10.1007/978-1-4757-4134-6

D. Kotzias, M. Denil, P. Blunsom, and N. De-freitas, Deep multi-instance transfer learning, p.3128, 1411.

P. Viola, J. C. Platt, and C. Zhang, Multiple instance boosting for object detection, NIPS, 2005.

H. Rowley, S. Baluja, and T. Kanade, Neural network-based face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.1, pp.23-38, 1998.
DOI : 10.1109/34.655647

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.5546

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), pp.886-893, 2005.
DOI : 10.1109/CVPR.2005.177

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

P. F. Felzenszwalb, R. B. Grishick, 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, 2010.
DOI : 10.1109/TPAMI.2009.167

L. Lin, Self-improving reactive agents based on reinforcement learning, planning and teaching, Machine learning, pp.293-321, 1992.
DOI : 10.1007/bf00992699

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.75.7884

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness et al., Human-level control through deep reinforcement learning, Nature, vol.101, issue.7540, pp.529-533, 2015.
DOI : 10.1016/S0004-3702(98)00023-X

V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap et al., Asynchronous methods for deep reinforcement learning, Proc. ICML, 2016.

P. Gronat, streetget -a small python package for building place recognition datasets, 2015.

A. Vedaldi and K. Lenc, Matconvnet ? convolutional neural networks for matlab, Proc. ACMM, 2015.

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

H. J. Kim, E. Dunn, and J. Frahm, Learned contextual feature reweighting for image geo-localization, Proc. CVPR, 2017.

A. Gordo, J. A. Rodríguez-serrano, F. Perronnin, and E. Valveny, Leveraging category-level labels for instance-level image retrieval, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6248035

A. Karpathy and L. Fei-fei, Deep visual-semantic alignments for generating image descriptions, Proc. CVPR, pp.2015-2016
DOI : 10.1109/tpami.2016.2598339

URL : http://arxiv.org/abs/1412.2306

. Image and . Netvlad, We compare our best trained network (VGG-16, f V LAD ), and the corresponding off-the-shelf network (whitening learnt on Pittsburgh, on standard image and object retrieval benchmarks, while varying the dimensionality (Dim.) of the image representation

M. Dim, Oxford 5k Paris 6k Holidays full crop full crop orig rot NetVLAD off-shelf 16 28

H. Azizpour, A. Razavian, J. Sullivan, A. Maki, and S. Carlsson, Factors of transferability from a generic ConvNet representation, 1406.

A. Babenko and V. Lempitsky, Aggregating local deep features for image retrieval, Proc. ICCV, 2015.

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

URL : http://arxiv.org/abs/1403.1840

A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, CNN Features Off-the-Shelf: An Astounding Baseline for Recognition, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 1403.
DOI : 10.1109/CVPRW.2014.131

URL : http://arxiv.org/abs/1403.6382

A. S. Razavian, J. Sullivan, A. Maki, and S. Carlsson, A baseline for visual instance retrieval with deep convolutional networks, Proc. ICLR, 2015.
DOI : 10.3169/mta.4.251

G. Tolias, R. Sicre, and H. Jégou, Particular object retrieval with integral max-pooling of CNN activations, Proc. ICLR, 2016.

Y. Kalantidis, C. Mellina, and S. Osindero, Cross-Dimensional Weighting for Aggregated Deep Convolutional Features, ECCV workshop on Webscale Vision and Social Media, 2016.
DOI : 10.1016/j.cviu.2013.12.002

URL : http://arxiv.org/abs/1512.04065

A. Oliva and A. Torralba, Modeling the shape of the scene: a holistic representation of the spatial envelope, 2001.

M. Douze, H. Jégou, H. Sandhawalia, L. Amsaleg, and C. Schmid, Evaluation of GIST descriptors for web-scale image search, Proceeding of the ACM International Conference on Image and Video Retrieval, CIVR '09, 2009.
DOI : 10.1145/1646396.1646421

URL : https://hal.archives-ouvertes.fr/inria-00394212