O. Chelly, T. Furon, S. Girard, E. Michael, K. Houle et al., Estimating local intrinsic dimensionality, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.29-38, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01159217

P. Arandjelovic, A. Gronat, T. Torii, J. Pajdla, and . Sivic, Netvlad: Cnn architecture for weakly supervised place recognition, CVPR, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01242052

K. Pankaj, S. Agarwal, . Har-peled, R. Kasturi, and . Varadarajan, Approximating extent measures of points, Journal of the ACM, vol.51, issue.4, pp.606-635, 2004.

M. Karim, . Abadir, R. Jan, and . Magnus, Matrix algebra, 2005.

K. [. Avrithis and . Rapantzikos, The medial feature detector: Stable regions from image boundaries, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126436

A. Ars-+-14-]-hossein-azizpour, J. Sharif-razavian, A. Sullivan, S. Maki, and . Carlsson, From generic to specific deep representations for visual recognition. arXiv preprint, 2014.

[. Arandjelovic and A. Zisserman, Multiple queries for large scale specific object retrieval, Procedings of the British Machine Vision Conference 2012, 2012.
DOI : 10.5244/C.26.92

R. Az12b, A. Arandjelovic, and . Zisserman, Three things everyone should know to improve object retrieval, CVPR, 2012.

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

]. R. Az14a, A. Arandjelovi?, and . Zisserman, Extremely low bit-rate nearest neighbor search using a set compression tree, Trans. PAMI, 2014.

[. Arandjelovic and A. Zisserman, DisLocation: Scalable Descriptor Distinctiveness for Location Recognition, ACCV, 2014.
DOI : 10.1007/978-3-319-16817-3_13

M. Christopher and . Bishop, Pattern recognition and machine learning, 2006.

M. Michael, J. Bronstein, Y. Bruna, A. Lecun, P. Szlam et al., Geometric deep learning: going beyond euclidean data, 2016.

H. Bcf-+-14-]-julien-bringer, M. Chabanne, A. Favre, T. Patey, M. Schneider et al., GSHADE: faster privacy-preserving distance computation and biometric identification, Proceedings of the 2Nd ACM Workshop on Information Hiding and Multimedia Security, pp.187-198, 2014.

[. Bringer, H. Chabanne, and A. Patey, SHADE: Secure HAmming DistancE Computation from Oblivious Transfer, Financial Cryptography and Data Security, pp.164-176, 2013.
DOI : 10.1007/978-3-642-41320-9_11
URL : https://hal.archives-ouvertes.fr/hal-00872212

[. Bay, A. Ess, T. Tuytelaars, and L. Van-gool, 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

J. [. Beyer, R. Goldstein, U. Ramakrishnan, and . Shaft, When Is ???Nearest Neighbor??? Meaningful?, Proceedings of the International Conference on Database Theory, pp.217-235, 1999.
DOI : 10.1007/3-540-49257-7_15

M. Brown, G. David, and . Lowe, Automatic Panoramic Image Stitching using Invariant Features, International Journal of Computer Vision, vol.50, issue.1, pp.59-73, 2007.
DOI : 10.1007/s11263-006-0002-3

A. Babenko and V. Lempitsky, The inverted multi-index, CVPR, 2012.

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

A. Babenko and V. Lempitsky, Efficient indexing of billion-scale datasets of deep descriptors, CVPR, 2016.

[. Borges, A. Mourão, and J. Magalhães, High-Dimensional Indexing by Sparse Approximation, Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, ICMR '15, 2015.
DOI : 10.1109/CVPR.2008.4587633

[. Boureau, J. Ponce, and Y. Lecun, A theoretical analysis of feature pooling in visual recognition, ICML, 2010.

L. Bo and C. Sminchisescu, Efficient match kernels between sets of features for visual recognition, NIPS, 2009.

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

[. Bhattarai, G. Sharma, and F. Jurie, CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.458
URL : https://hal.archives-ouvertes.fr/hal-01301381

J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, Spectral networks and locally connected networks on graphs, 2013.

G. Csurka, C. Dance, L. Fan, J. Willamowski, and C. Bray, Visual categorization with bags of keypoints, ECCV Workshop Statistical Learning in Computer Vision, 2004.

S. Moses and . Charikar, Similarity estimation techniques from rounding algorithms, STOC, pp.380-388, 2002.

R. Fan and . Chung, Spectral graph theory, American Mathematical Soc, vol.92, 1997.

[. Chandra and I. Kokkinos, Fast, Exact and Multi-scale Inference for Semantic Image Segmentation with Deep Gaussian CRFs, ECCV, pp.402-418, 2016.
DOI : 10.1109/ICCV.2015.304
URL : https://hal.archives-ouvertes.fr/hal-01410872

H. Thomas, . Cormen, E. Charles, . Leiserson, L. Ronald et al., Introduction to algorithms, 2009.

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

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

P. [. Cummins and . Newman, Fab-map: Appearance-based place recognition and mapping using a learned visual vocabulary model, ICML, 2010.

. O. Bibliography, J. Chum, J. Philbin, M. Sivic, A. Isard et al., Total recall: Automatic query expansion with a generative feature model for object retrieval, ICCV, 2007.

S. Cao and N. Snavely, Graph-based discriminative learning for location recognition, CVPR, 2013.

. Ctc-+-09-]-v, G. Chandrasekhar, D. Takacs, S. Chen, R. Tsai et al., CHoG: compressed histogram of gradients, CVPR, 2009.

J. Olivier-chapelle, B. Weston, and . Scholkopf, Cluster kernels for semi-supervised learning, NIPS, pp.601-608, 2003.

[. Chen, L. Zheng, X. Hu, and P. Zhou, Discriminative saliency propagation with sink points, Pattern Recognition, vol.60, pp.2-12, 2016.
DOI : 10.1016/j.patcog.2016.05.016

M. Donoser and H. Bischof, Diffusion Processes for Retrieval Revisited, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.174

X. Defferrard, P. Bresson, and . Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering, NIPS, pp.3837-3845, 2016.

W. Dong, M. Charikar, and K. Li, Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pp.123-130, 2008.
DOI : 10.1145/1390334.1390358

W. Dong, M. Charikar, and K. Li, Efficient k-nearest neighbor graph construction for generic similarity measures, Proceedings of the 20th international conference on World wide web, WWW '11, 2011.
DOI : 10.1145/1963405.1963487

[. 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

N. [. Datar, P. Immorlica, V. S. Indyk, and . Mirrokni, Locality-sensitive hashing scheme based on p-stable distributions, Proceedings of the twentieth annual symposium on Computational geometry , SCG '04, 2004.
DOI : 10.1145/997817.997857

A. Delvinioti, H. Jégou, L. Amsaleg, and M. Houle, Image retrieval with reciprocal and shared nearest neighbors, VISAPP, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00907481

S. Inderjit, . Dhillon, S. Dharmendra, and . Modha, Concept decompositions for large sparse text data using clustering, Machine learning, vol.42, issue.12, pp.143-175, 2001.

[. Drineas, W. Michael, and . Mahoney, On the Nyström method for approximating a gram matrix for improved kernel-based learning, Journal of Machine Learning Research, vol.6, pp.2153-2175, 2005.

M. Geoffrey, . Davis, G. Stephane, Z. Mallat, and . Zhang, Adaptive timefrequency decompositions with matching pursuit, SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, pp.402-413, 1994.

[. Donoser, Replicator Graph Clustering, Procedings of the British Machine Vision Conference 2013, 2013.
DOI : 10.5244/C.27.38

A. Egozi, Y. Keller, and H. Guterman, Improving Shape Retrieval by Spectral Matching and Meta Similarity, IEEE Transactions on Image Processing, vol.19, issue.5, pp.1319-1327, 2010.
DOI : 10.1109/TIP.2010.2040448

[. Elhamifar, G. Sapiro, and R. Vidal, See all by looking at a few: Sparse modeling for finding representative objects, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
DOI : 10.1109/CVPR.2012.6247852

J. H. Friedman, J. L. Bentley, and R. A. Finkel, An Algorithm for Finding Best Matches in Logarithmic Expected Time, ACM Transactions on Mathematical Software, vol.3, issue.3, pp.209-226, 1977.
DOI : 10.1145/355744.355745

[. Feldman, M. Feigin, and N. Sochen, Learning Big (Image) Data via Coresets for Dictionaries, Journal of Mathematical Imaging and Vision, vol.44, issue.5, pp.276-291, 2013.
DOI : 10.1137/1.9781611973099.105

[. Fujiwara, M. Nakatsuji, M. Onizuka, and M. Kitsuregawa, Fast and exact top-k search for random walk with restart, Proceedings of the VLDB Endowment, pp.442-453, 2012.
DOI : 10.14778/2140436.2140441

[. Gordo, J. Almazan, J. Revaud, and D. Larlus, Deep Image Retrieval: Learning Global Representations for Image Search, p.2016
DOI : 10.1109/CVPR.2014.180

[. Gordo, J. Almazan, J. Revaud, and D. Larlus, Endto-end learning of deep visual representations for image retrieval, 2016.

P. [. Gionis, R. Indyk, and . Motwani, Similarity search in high dimension via hashing, Proceedings of the International Conference on Very Large DataBases, pp.518-529, 1999.

[. Gosselin, N. Murray, H. Jégou, and F. Perronnin, Revisiting the fisher vector for fine-grained classification. Pattern recognition letters, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01056223

A. Gordo and F. Perronnin, Asymmetric distances for binary embeddings, CVPR, 2011.

L. Grady, Random Walks for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.11, pp.1768-1783, 2006.
DOI : 10.1109/TPAMI.2006.233

[. Gong, L. Wang, R. Guo, and S. Lazebnik, Multiscale orderless pooling of deep convolutional activation features, ECCV, 2014.

[. Hackbusch, Iterative solution of large sparse systems of equations, 1994.

J. Henaff, Y. Bruna, and . Lecun, Deep convolutional networks on graph-structured data. arXiv preprint

J. Hays and A. A. Efros, IM2GPS: estimating geographic information from a single image, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587784

N. [. Hadid, S. Evans, J. Marcel, and . Fierrez, Biometrics Systems Under Spoofing Attack: An evaluation methodology and lessons learned, IEEE Signal Processing Magazine, vol.32, issue.5, p.20, 2015.
DOI : 10.1109/MSP.2015.2437652

[. Halko, . Per-gunnar-martinsson, and . Tropp, Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions, SIAM Review, vol.53, issue.2, pp.217-288, 2011.
DOI : 10.1137/090771806

B. Gary, M. Huang, T. Ramesh, E. Berg, and . Learned-miller, Labeled faces in the wild: A database for studying face recognition in unconstrained environments, 2007.

C. Harris and M. Stephens, A Combined Corner and Edge Detector, Procedings of the Alvey Vision Conference 1988, 1988.
DOI : 10.5244/C.2.23

J. Heinly, L. Johannes, E. Schonberger, J. Dunn, and . Frahm, Reconstructing the world* in six days*(as captured by the yahoo 100 million image dataset), CVPR, 2015.

H. Charles and . Hubbell, An input-output approach to clique identification, Sociometry, 1965.

K. David, P. Hammond, R. Vandergheynst, and . Gribonval, Wavelets on graphs via spectral graph theory, Applied and Computational Harmonic Analysis, vol.30, issue.2, pp.129-150, 2011.

[. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.90

A. Iscen, T. Furon, V. Gripon, M. Rabbat, and H. Jégou, Memory vectors for similarity search in high-dimensional spaces, IEEE Transactions on Big Data, 2017.
DOI : 10.1109/TBDATA.2017.2677964
URL : https://hal.archives-ouvertes.fr/hal-01481220

P. Indyk and R. Motwani, Approximate nearest neighbors, Proceedings of the thirtieth annual ACM symposium on Theory of computing , STOC '98, pp.604-613, 1998.
DOI : 10.1145/276698.276876

A. Iscen, M. Rabbat, and T. Furon, Efficient Large-Scale Similarity Search Using Matrix Factorization, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.228
URL : https://hal.archives-ouvertes.fr/hal-01294736

A. Iscen and G. Tolias, Yannis Avrithis, Teddy Furon, and Ondrej Chum. Efficient diffusion on region manifolds: Recovering small objects with compact cnn representations, CVPR, 2017.

A. Iscen, G. Tolias, P. Gosselin, and H. Jégou, A Comparison of Dense Region Detectors for Image Search and Fine-Grained Classification, IEEE Transactions on Image Processing, vol.24, issue.8, pp.24-2015
DOI : 10.1109/TIP.2015.2423557
URL : https://hal.archives-ouvertes.fr/hal-01143201

A. Irschara, C. Zach, J. Frahm, and H. Bischof, From structure-from-motion point clouds to fast location recognition, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206587

Y. Jing and S. Baluja, VisualRank: Applying PageRank to Large-Scale Image Search, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.11, pp.1877-1890, 2008.
DOI : 10.1109/TPAMI.2008.121

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

[. Jégou, M. Douze, and C. Schmid, Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search, ECCV, 2008.
DOI : 10.1109/CVPR.2007.383150

[. Jégou, M. Douze, and C. Schmid, On the burstiness of visual elements, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206609

[. Jégou, 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

[. 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

[. 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

D. [. Jaakkola and . Haussler, Exploiting generative models in discriminative classifiers, NIPS, 1998.

[. 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

[. Jain, H. Jégou, and P. Gros, Asymmetric hamming embedding, Proceedings of the 19th ACM international conference on Multimedia, MM '11, 2011.
DOI : 10.1145/2072298.2072035
URL : https://hal.archives-ouvertes.fr/inria-00607278

H. Jegou, F. Perronnin, M. Douze, J. Sánchez, P. Perez et al., Aggregating Local Image Descriptors into Compact Codes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.9, pp.1704-1716, 2012.
DOI : 10.1109/TPAMI.2011.235
URL : https://hal.archives-ouvertes.fr/inria-00633013

J. Jung, K. Shin, L. Sael, and U. Kang, Random Walk with Restart on Large Graphs Using Block Elimination, ACM Transactions on Database Systems, vol.41, issue.2, p.12, 2016.
DOI : 10.1145/2396761.2398450

H. Jégou and A. Zisserman, Triangulation embedding and democratic kernels for image search, CVPR, 2014.

L. Katz, A new status index derived from sociometric analysis, Psychometrika, vol.13, issue.1, pp.39-43, 1953.
DOI : 10.1007/BF02289026

[. Kontschieder, M. Donoser, and H. Bischof, Beyond Pairwise Shape Similarity Analysis, ACCV, 2009.
DOI : 10.1007/978-3-642-12297-2_63

[. Kang and C. Faloutsos, Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining, 2011 IEEE 11th International Conference on Data Mining, pp.300-309, 2011.
DOI : 10.1109/ICDM.2011.26

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

J. [. Kondor and . Lafferty, Diffusion kernels on graphs and other discrete structures, ICML, 2002.

. Tae-hoon-kim, S. U. Kyoung-mu-lee, and . Lee, Generative image segmentation using random walks with restart, ECCV, pp.264-275, 2008.

[. Kalantidis, C. Mellina, and S. Osindero, Crossdimensional weighting for aggregated deep convolutional features. arXiv preprint, 2015.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Communications of the ACM, vol.60, issue.6, 2012.
DOI : 10.1162/neco.2009.10-08-881

J. Kandola, J. Shawe-taylor, and N. Cristianini, Learning semantic similarity, NIPS, 2002.

R. Kondor and J. Vert, Diffusion kernels, Kernel Methods in Computational Biology, pp.171-192, 2004.

]. T. Lin98 and . Lindeberg, Feature detection with automatic scale selection, IJCV, vol.30, issue.2, pp.77-116, 1998.

. Ljw-+-07-]-q, W. Lv, Z. Josephson, M. Wang, K. Charikar et al., Multi-probe LSH: Efficient indexing for high-dimensional similarity search, Proceedings of the International Conference on Very Large DataBases, pp.950-961, 2007.

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

N. Amy, . Langville, D. Carl, and . Meyer, Deeper inside PageRank, Internet Mathematics, vol.1, issue.3, pp.335-380, 2004.

[. Lu, V. Mahadevan, and N. Vasconcelos, Learning Optimal Seeds for Diffusion-Based Salient Object Detection, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.357

]. D. Low04 and . Lowe, Distinctive image features from scale-invariant keypoints, IJCV, vol.60, issue.2, pp.91-110, 2004.

[. Li, N. Snavely, D. Huttenlocher, and P. Fua, Worldwide pose estimation using 3d point clouds, ECCV, 2012.

A. John, M. Lee, and . Verleysen, Nonlinear dimensionality reduction, 2007.

[. Mopuri, R. Venkatesh, and . Babu, Object level deep feature pooling for compact image representation, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015.
DOI : 10.1109/CVPRW.2015.7301273

F. Monti, D. Boscaini, J. Masci, E. R. Svoboda, M. Michael et al., Geometric deep learning on graphs and manifolds using mixture model cnns. arXiv preprint, 2016.

J. Mairal, F. Bach, and J. Ponce, Sparse modeling for image and vision processing. arXiv preprint, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01081139

[. Mairal, F. Bach, J. Ponce, and G. Sapiro, Online learning for matrix factorization and sparse coding, The Journal of Machine Learning Research, vol.11, pp.19-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00408716

A. Mikulik, O. Chum, and J. Matas, Image Retrieval for Online Browsing in Large Image Collections, International Conference on Similarity Search and Applications, 2013.
DOI : 10.1007/978-3-642-41062-8_2

J. Matas, O. Chum, U. Martin, and T. Pajdla, Robust wide baseline stereo from maximally stable extremal regions, BMVC, pp.384-393, 2002.

M. Muja and D. G. Lowe, Scalable Nearest Neighbor Algorithms for High Dimensional Data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.11, p.36, 2014.
DOI : 10.1109/TPAMI.2014.2321376

[. Murray and F. Perronnin, Generalized Max Pooling, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.317

[. Mikolajczyk and C. Schmid, Scale & Affine Invariant Interest Point Detectors, International Journal of Computer Vision, vol.60, issue.1, pp.63-86, 2004.
DOI : 10.1023/B:VISI.0000027790.02288.f2
URL : https://hal.archives-ouvertes.fr/inria-00548554

T. Mts-+-05-]-krystian-mikolajczyk, C. Tuytelaars, A. Schmid, J. Zisserman, F. Matas et al., A Comparison of Affine Region Detectors, International Journal of Computer Vision, vol.65, issue.1-2, pp.43-72, 2005.
DOI : 10.1007/s11263-005-3848-x

A. [. Mikolajczyk, C. Zisserman, and . Schmid, Shape recognition with edge-based features, Procedings of the British Machine Vision Conference 2003, 2003.
DOI : 10.5244/C.17.79
URL : https://hal.archives-ouvertes.fr/inria-00548226

E. Nowak, F. Jurie, and B. Triggs, Sampling strategies for bagof-features image classification, ECCV, pp.490-503, 2006.

B. Nadler, S. Lafon, R. Ronald, . Coifman, G. Ioannis et al., Diffusion maps, spectral clustering and eigenfunctions of fokker-planck operators, NIPS, 2005.

[. 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

D. Nistér and H. Stewénius, 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

J. Nocedal and S. Wright, Numerical optimization, 2006.
DOI : 10.1007/b98874

V. Alan, . Oppenheim, W. Ronald, and . Schafer, Discrete-Time Signal Processing: Pearson New International Edition, 2010.

[. Page, S. Brin, R. Motwani, and T. Winograd, The PageRank citation ranking: bringing order to the web, 1999.

]. J. Pci-+-07, O. Philbin, M. Chum, J. Isard, A. Sivic et al., Object retrieval with large vocabularies and fast spatial matching, CVPR, 2007.

]. J. Pci-+-08, O. Philbin, M. Chum, J. Isard, A. Sivic et al., Lost in quantization: Improving particular object retrieval in large scale image databases, CVPR, 2008.

[. Perronnin and C. R. 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

J. [. Perronnin, T. Sánchez, and . Mensink, Improving the Fisher Kernel for Large-Scale Image Classification, ECCV, 2010.
DOI : 10.1007/978-3-642-15561-1_11
URL : https://hal.archives-ouvertes.fr/inria-00548630

[. Puy, S. Kitic, and P. Pérez, Unifying local and non-local signal processing with graph cnns. arXiv preprint arXiv:1702.07759, 2017. [PRK93] Yagyensh Chandra Pati, Ramin Rezaiifar, and PS Krishnaprasad. Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition, ASILOMAR, pp.40-44, 1993.

J. Florent-perronnin, Y. Sánchez, and . Liu, Large-scale image categorization with explicit data embedding, CVPR, 2010.

G. Pvg-+-11-]-fabian-pedregosa, A. Varoquaux, V. Gramfort, B. Michel, O. Thirion et al., al. Scikit-learn: Machine learning in python, The Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.

J. Pan, H. Yang, C. Faloutsos, and P. Duygulu, Automatic multimedia cross-modal correlation discovery, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, 2004.
DOI : 10.1145/1014052.1014135

S. Gammeter and L. Bossard, Till Quack, and Luc Van Gool. Hello neighbor: Accurate object retrieval with k-reciprocal nearest neighbors, CVPR, 2011.

[. Richardson, M. Pedro, and . Domingos, The intelligent surfer: Probabilistic combination of link and content information in PageRank, NIPS, 2001.

L. Havard-rue and . Held, Gaussian Markov random fields: theory and applications, 2005.

S. [. Raginsky and . Lazebnik, Locality-sensitive binary codes from shiftinvariant kernels, NIPS, 2010.

[. Rao, S. Kumar, and M. , Generalized inverse of a matrix and its applications, Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability, 1972.

S. Ali, J. Razavian, S. Sullivan, A. Carlsson, and . Maki, Visual instance retrieval with deep convolutional networks, ITE Transactions on Media Technology and Applications, vol.4, pp.251-258, 2016.

A. Vladimir-rokhlin, M. Szlam, and . Tygert, A Randomized Algorithm for Principal Component Analysis, SIAM Journal on Matrix Analysis and Applications, vol.31, issue.3, pp.1100-1124, 2009.
DOI : 10.1137/080736417

. Sg-roux, . Tremblay, . Borgnat, . Abry, P. Wendt et al., Multiscale anisotropic texture unsupervised clustering for photographic paper, IEEE International Workshop on Information Forensics and Security, pp.1-6, 2015.

[. Radenovi?, Giorgos Tolias, and Ond?ej Chum. CNN image retrieval learns from bow: Unsupervised fine-tuning with hard examples, p.2016

[. Shi, Y. Avrithis, and H. Jégou, Early burst detection for memory-efficient image retrieval, CVPR, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01146533

M. [. Schindler, R. Brown, and . Szeliski, City-Scale Location Recognition, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383150

D. Sculley, Web-scale k-means clustering, Proceedings of the 19th international conference on World wide web, WWW '10, 2010.
DOI : 10.1145/1772690.1772862

R. John and . Seeley, The net of reciprocal influence. a problem in treating sociometric data, Canadian Journal of Experimental Psychology, vol.3, p.234, 1949.

[. Shi, T. Furon, and H. Jégou, A Group Testing Framework for Similarity Search in High-dimensional Spaces, Proceedings of the ACM International Conference on Multimedia, MM '14, 2014.
DOI : 10.1023/B:VISI.0000029664.99615.94
URL : https://hal.archives-ouvertes.fr/hal-01062531

[. Sattler, M. Havlena, K. Schindler, and M. Pollefeys, Large-Scale Location Recognition and the Geometric Burstiness Problem, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.175

R. Sicre and H. Jégou, Memory Vectors for Particular Object Retrieval with Multiple Queries, Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, ICMR '15, 2015.
DOI : 10.1145/1178677.1178722

J. Alexander, R. Smola, and . Kondor, Kernels and regularization on graphs, Learning Theory and Kernel Machines, pp.144-158, 2003.

X. Shen, Z. Lin, J. Brandt, and Y. Wu, Spatially-Constrained Similarity Measurefor Large-Scale Object Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.6, pp.1229-1241, 2014.
DOI : 10.1109/TPAMI.2013.237

[. Sattler, B. Leibe, and L. Kobbelt, Improving Image-Based Localization by Active Correspondence Search, ECCV, 2012.
DOI : 10.1007/978-3-642-33718-5_54

G. Salton and M. J. Mcgill, Introduction to modern information retrieval, 1983.

A. Sandryhaila, M. Jose, and . Moura, Discrete Signal Processing on Graphs, IEEE Transactions on Signal Processing, vol.61, issue.7, pp.611644-1656, 2013.
DOI : 10.1109/TSP.2013.2238935

I. David, . Shuman, K. Sunil, P. Narang, A. Frossard et al., The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Processing Magazine, vol.30, issue.3, pp.83-98, 2013.

F. Sánchez, T. Perronnin, J. Mensink, and . Verbeek, Image Classification with the Fisher Vector: Theory and Practice, International Journal of Computer Vision, vol.73, issue.2, pp.222-245, 2013.
DOI : 10.1007/s11263-006-9794-4

O. [. Simonyan, A. Parkhi, A. Vedaldi, and . Zisserman, Fisher Vector Faces in the Wild, Procedings of the British Machine Vision Conference 2013, 2013.
DOI : 10.5244/C.27.8

L. Johannes, F. Schonberger, O. Radenovic, J. Chum, and . Frahm, From single image query to detailed 3d reconstruction, CVPR, 2015.

J. Alex, B. Smola, K. Scholkopf, and . Muller, The connection between regularization operators and support vector kernels, Neural Networks, vol.11, issue.4, pp.637-649, 1998.

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

I. David, P. Shuman, P. Vandergheynst, and . Frossard, Chebyshev polynomial approximation for distributed signal processing, International Conference on Distributed Computing in Sensor Systems and Workshops, pp.1-8, 2011.

A. [. Simonyan, A. Vedaldi, and . Zisserman, Learning Local Feature Descriptors Using Convex Optimisation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.8, 2013.
DOI : 10.1109/TPAMI.2014.2301163

[. Sattler, T. Weyand, B. Leibe, and L. Kobbelt, Image Retrieval for Image-Based Localization Revisited, Procedings of the British Machine Vision Conference 2012, 2012.
DOI : 10.5244/C.26.76

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

[. Schneider and M. Zohner, GMW vs. Yao? Efficient Secure Two-Party Computation with Low Depth Circuits, Financial Cryptography and Data Security, 2013.
DOI : 10.1007/978-3-642-39884-1_23

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition. ICLR, 2014.

[. 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

A. Torii, R. Arandjelovic, J. Sivic, M. Okutomi, and T. Pajdla, 24/7 place recognition by view synthesis Graph wavelets for multiscale community mining, IEEE Transactions on Signal Processing, issue.20, pp.625227-5239, 2014.

[. Tong, C. Faloutsos, Y. Jia, and . Pan, Fast Random Walk with Restart and Its Applications, Sixth International Conference on Data Mining (ICDM'06), pp.613-622, 2006.
DOI : 10.1109/ICDM.2006.70

[. Tolias and H. Jégou, Visual query expansion with or without geometry: Refining local descriptors by feature aggregation, Pattern Recognition, vol.47, issue.10, pp.3466-3476, 2014.
DOI : 10.1016/j.patcog.2014.04.007
URL : https://hal.archives-ouvertes.fr/hal-00971267

D. [. Turcot and . 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

F. Marshall, C. Tappen, . Liu, H. Edward, . Adelson et al., Learning Gaussian conditional random fields for low-level vision, CVPR, pp.1-8, 2007.

D. A. Bart-thomee, G. Shamma, B. Friedland, K. Elizalde, D. Ni et al., Yfcc100m: The new data in multimedia research, Commun. ACM, vol.59, issue.2, p.2016

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

A. Torii, J. Sivic, and T. Pajdla, Visual localization by linear combination of image descriptors, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011.
DOI : 10.1109/ICCVW.2011.6130230
URL : https://hal.archives-ouvertes.fr/hal-01053880

[. Torii, J. Sivic, T. Pajdla, and M. Okutomi, Visual place recognition with repetitive structures, CVPR, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00934288

[. Vigna, Spectral ranking. arXiv preprint arXiv:0912.0238 [Vis12] Nisheeth K Vishnoi. Laplacian solvers and their algorithmic applications, Theoretical Computer Science, vol.8, issue.12, pp.1-141, 2009.

[. Vichy, N. Vishwanathan, N. Nicol, R. Schraudolph, K. M. Kondor et al., Graph kernels, Journal of Machine Learning Research, vol.11, pp.1201-1242, 2010.

S. Winder and M. Brown, Learning Local Image Descriptors, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.382971

R. Witten and E. Candes, Randomized algorithms for lowrank matrix factorizations: Sharp performance bounds. arXiv preprint, 2013.

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

M. [. Wainwright and . Jordan, Graphical Models, Exponential Families, and Variational Inference, Machine Learning, 2008.
DOI : 10.1561/2200000001

S. Wang and S. Jiang, INSTRE, ACM Transactions on Multimedia Computing, Communications, and Applications, vol.11, issue.3, p.37, 2015.
DOI : 10.1109/TPAMI.2009.132

T. Weyand and B. Leibe, Discovering favorite views of popular places with iconoid shift, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126361

J. Wang, G. Qi, N. Sebe, and C. Aggarwal, Special issue on big media data: Understanding, search, and mining, IEEE Trans. on Big Data, vol.1, issue.3, 2015.

R. Weber, H. Schek, and S. Blott, A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces, Proceedings of the International Conference on Very Large DataBases, pp.194-205, 1998.

[. Weiss, A. Torralba, and R. Fergus, Spectral hashing, NIPS, 2009.

[. Xie, R. Hong, B. Zhang, and Q. Tian, Image Classification and Retrieval are ONE, Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, ICMR '15, 2015.
DOI : 10.1145/2393347.2393377

L. Xie, Q. Tian, W. Zhou, and B. Zhang, Fast and accurate near-duplicate image search with affinity propagation on the ImageWeb, Computer Vision and Image Understanding, vol.124, 2014.
DOI : 10.1016/j.cviu.2013.12.011

X. Yang, S. Koknar-tezel, and L. J. Latecki, Locally constrained diffusion process on locally densified distance spaces with applications to shape retrieval, CVPR, 2009.

[. Yang, B. Matei, S. Larry, and . Davis, Re-ranking by Multi-feature Fusion with Diffusion for Image Retrieval, 2015 IEEE Winter Conference on Applications of Computer Vision, 2015.
DOI : 10.1109/WACV.2015.82

O. Zhou, T. N. Bousquet, J. Lal, B. Weston, and . Schölkopf, Learning with local and global consistency, NIPS, 2003.

. Zernike, Beugungstheorie des schneidenver-fahrens und seiner verbesserten form, der phasenkontrastmethode, Physica, vol.1, issue.7-12, pp.689-704, 1934.
DOI : 10.1016/S0031-8914(34)80259-5

[. Zhu, Z. Ghahramani, J. Lafferty-zhang, and J. Kosecka, Semi-supervised learning using Gaussian fields and harmonic functions Image based localization in urban environments, ICML 3DPVT, 2003.

[. Zhu, J. Kandola, J. Lafferty, and Z. Ghahramani, Graph kernels by spectral transforms, Semi-Supervised Learning, pp.277-291, 2006.

[. Zhu, D. John, Z. Lafferty, and . Ghahramani, Semi-supervised Learning, 2003.
DOI : 10.3115/981658.981684
URL : https://hal.archives-ouvertes.fr/hal-01479213

J. Zhou, A. Weston, O. Gretton, B. Bousquet, and . Schölkopf, Ranking on data manifolds, NIPS, 2003.

M. Zhang, T. Yang, K. Cour, D. N. Yu, and . Metaxas, Query Specific Fusion for Image Retrieval, ECCV, 2012.
DOI : 10.1007/978-3-642-33709-3_47

I. Query-examples-from and .. Oxford, Paris datasets and retrieved images ranked by decreasing order of ranking difference between global and regional diffusion We measure precision at the position where each image is retrieved and report this under each image for global and regional diffusion Average Precision (AP) is reported per query for the two methods, p.100