S. Abiteboul, Issues in Monitoring Web Data, Database and Expert Systems Applications (DEXA), pp.51-69, 2002.
DOI : 10.1007/3-540-46146-9_1

. Adar, Resonance on the web, Proceedings of the 27th international conference on Human factors in computing systems, CHI 09, p.72, 2009.
DOI : 10.1145/1518701.1518909

. Adar, The web changes everything, Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM '09, p.74, 2009.
DOI : 10.1145/1498759.1498837

. Agarwal, Generalized non-metric multidimensional scaling, International Conference on Artificial Intelligence and Statistics (AISTATS, pp.11-18, 2007.

. Akata, Label-Embedding for Attribute-Based Classification, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.819-826, 2013.
DOI : 10.1109/CVPR.2013.111

URL : https://hal.archives-ouvertes.fr/hal-00815747

. Avila, Pooling in image representation: The visual codeword point of view, Computer Vision and Image Understanding, vol.117, issue.5, pp.453-465, 2013.
DOI : 10.1016/j.cviu.2012.09.007

URL : https://hal.archives-ouvertes.fr/hal-01172709

. Bach, Multiple kernel learning, conic duality, and the SMO algorithm, Twenty-first international conference on Machine learning , ICML '04, p.6, 2004.
DOI : 10.1145/1015330.1015424

. Bartlett, Convexity, Classification, and Risk Bounds, Journal of the American Statistical Association, vol.101, issue.473, pp.138-156, 2006.
DOI : 10.1198/016214505000000907

. Bellet, A Survey on Metric Learning for Feature Vectors and Structured Data. ArXiv e-prints, p.17, 2013.

B. Saad, G. Saad, M. Gançarski, and S. , Archiving the web using page changes patterns: a case study, JCDL. 66, p.72, 2011.
DOI : 10.1007/s00799-012-0094-z

. Bengio, Representation learning: A review and new perspectives. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.8, pp.351798-1828, 2013.

. Bengio, Greedy layer-wise training of deep networks Advances in neural information processing systems, NIPS), vol.19, pp.153-162, 2007.

I. Biederman, Recognition-by-components: A theory of human image understanding., Psychological Review, vol.94, issue.2, pp.115-118, 1987.
DOI : 10.1037/0033-295X.94.2.115

G. Borg, I. Borg, and P. Groenen, Modern Multidimensional Scaling: Theory and Applications, Journal of Educational Measurement, vol.40, issue.3, p.11, 2005.
DOI : 10.1007/BF02289341

. Boureau, Learning mid-level features for recognition, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2559-2566, 2010.
DOI : 10.1109/CVPR.2010.5539963

. Boyd, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Machine Learning, pp.1-122, 2011.
DOI : 10.1561/2200000016

V. Boyd, S. P. Boyd, and L. Vandenberghe, Convex optimization, p.90, 2004.

. Cai, Vips: a vision-based page segmentation algorithm, 2003.

. Candès, Enhancing sparsity by reweighted l1 minimization, Journal of Fourier analysis and applications, vol.14, issue.62, pp.5-6877, 2008.

O. Chapelle, Training a Support Vector Machine in the Primal, Neural Computation, vol.6, issue.5, pp.1155-1178, 2007.
DOI : 10.1198/106186005X25619

O. Chapelle and S. S. Keerthi, Efficient algorithms for ranking with SVMs, Information Retrieval, vol.6, issue.6, pp.201-215, 2010.
DOI : 10.1007/s10791-009-9109-9

. Chatfield, The devil is in the details: an evaluation of recent feature encoding methods, Procedings of the British Machine Vision Conference 2011, 2011.
DOI : 10.5244/C.25.76

. Chechik, An online algorithm for large scale image similarity learning, Advances in Neural Information Processing Systems (NIPS), pp.306-314, 2009.

. Chechik, Large Scale Online Learning of Image Similarity through Ranking, JMLR, vol.11, issue.20, pp.1109-1135, 2010.
DOI : 10.1007/978-3-642-02172-5_2

G. Cho, J. Cho, and H. Garcia-molina, The evolution of the web and implications for an incremental crawler, p.67, 2000.

. Chopra, Learning a Similarity Metric Discriminatively, with Application to Face Verification, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.539-546, 2005.
DOI : 10.1109/CVPR.2005.202

N. Coates, A. Coates, and A. Y. Ng, The importance of encoding versus training with sparse coding and vector quantization, Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp.921-928, 2011.

V. Cortes, C. Cortes, and V. Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995.
DOI : 10.1007/BF00994018

H. Cover, P. Hart, K. Crammer, and Y. Singer, Nearest neighbor pattern classification Information Theory On the algorithmic implementation of multiclass kernel-based vector machines, IEEE Transactions on The Journal of Machine Learning Research, vol.13, issue.2, pp.21-27265, 1967.

. Criminisi, Region Filling and Object Removal by Exemplar-Based Image Inpainting, IEEE Transactions on Image Processing, vol.13, issue.9, pp.1200-1212, 2004.
DOI : 10.1109/TIP.2004.833105

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.9434

D. Cula, O. G. Cula, and K. J. Dana, 3D Texture Recognition Using Bidirectional Feature Histograms, International Journal of Computer Vision, vol.59, issue.1, pp.33-60, 2004.
DOI : 10.1023/B:VISI.0000020670.05764.55

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.83.6620

J. Dattorro, Convex optimization and Euclidean distance geometry, p.53, 2005.

. Davis, Informationtheoretic metric learning, International Conference on Machine Learning (ICML, pp.26-31, 2007.

. Deng, Hierarchical semantic indexing for large scale image retrieval, CVPR 2011, pp.785-792, 2011.
DOI : 10.1109/CVPR.2011.5995516

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.225.6901

. Deng, Imagenet: A large-scale hierarchical image database, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, p.45, 2009.

. Douze, Evaluation of GIST descriptors for web-scale image search, Proceeding of the ACM International Conference on Image and Video Retrieval, CIVR '09, p.71, 2009.
DOI : 10.1145/1646396.1646421

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

. Duchenne, A graph-matching kernel for object categorization, 2011 International Conference on Computer Vision, pp.1792-1799, 2011.
DOI : 10.1109/ICCV.2011.6126445

URL : https://hal.archives-ouvertes.fr/hal-00650345

K. Fan, On a theorem of weyl concerning eigenvalues of linear transformations i, Proceedings of the National Academy of Sciences of the United States of America, pp.35652-53, 1949.

. Farhadi, Describing objects by their attributes, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.1778-1785, 2009.
DOI : 10.1109/CVPR.2009.5206772

M. Fazel, Matrix rank minimization with applications, p.50, 2002.

. Fei-fei, Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories, Computer Vision and Image Understanding, vol.106, issue.1, pp.59-70, 2007.
DOI : 10.1016/j.cviu.2005.09.012

P. Fei-fei, L. Fei-fei, and P. Perona, A Bayesian Hierarchical Model for Learning Natural Scene Categories, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.524-531, 2005.
DOI : 10.1109/CVPR.2005.16

. Feng, Geometric lp-norm feature pooling for image classification, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2609-2704, 2011.

R. A. Fisher, THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTS, Annals of Eugenics, vol.8, issue.4, pp.376-386, 1938.
DOI : 10.1111/j.1469-1809.1938.tb02189.x

S. Flesca and E. Masciari, Efficient and effective Web change detection, Data & Knowledge Engineering, vol.46, issue.2, pp.203-224, 2003.
DOI : 10.1016/S0169-023X(02)00210-0

S. Freund, Y. Freund, and R. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Computational learning theory, pp.23-37, 1995.
DOI : 10.1006/jcss.1997.1504

. Friedman, Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors), The Annals of Statistics, vol.28, issue.2, pp.337-407, 2000.
DOI : 10.1214/aos/1016218223

. Frome, Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification, 2007 IEEE 11th International Conference on Computer Vision, p.23, 2007.
DOI : 10.1109/ICCV.2007.4408839

. Galton and F. Galton, Natural inheritance, p.11, 1889.

. Geman, Neural Networks and the Bias/Variance Dilemma, Neural Computation, vol.36, issue.1, pp.1-58, 1992.
DOI : 10.1162/neco.1990.2.1.1

. Globerson, A. Globerson, and S. Roweis, Metric learning by collapsing classes Advances in neural information processing systems (NIPS), p.14, 2006.

. Globerson, A. Globerson, and S. T. Roweis, Visualizing pairwise similarity via semidefinite programming, International Conference on Artificial Intelligence and Statistics, pp.139-146, 2007.

. Goh, Unsupervised and Supervised Visual Codes with Restricted Boltzmann Machines, Computer Vision?ECCV 2012, pp.298-311, 2012.
DOI : 10.1007/978-3-642-33715-4_22

URL : https://hal.archives-ouvertes.fr/hal-00816428

. Goldberger, Neighbourhood components analysis, Advances in neural information processing systems (NIPS). 2, 3, p.23, 2004.

A. A. Goodrum, Image information retrieval: An overview of current research, Informing Science, vol.3, issue.2 10, pp.63-66, 2000.

. Grauman, K. Grauman, and T. Darrell, The pyramid match kernel: discriminative classification with sets of image features, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.1458-1465, 2005.
DOI : 10.1109/ICCV.2005.239

. Guillaumin, Is that you? Metric learning approaches for face identification, 2009 IEEE 12th International Conference on Computer Vision, pp.20-59, 2009.
DOI : 10.1109/ICCV.2009.5459197

URL : https://hal.archives-ouvertes.fr/inria-00439290

S. Harris, C. Harris, and M. Stephens, A Combined Corner and Edge Detector, Procedings of the Alvey Vision Conference 1988, p.50, 1988.
DOI : 10.5244/C.2.23

. Hinton, A Fast Learning Algorithm for Deep Belief Nets, Neural Computation, vol.18, issue.7, pp.1527-1554, 2006.
DOI : 10.1162/jmlr.2003.4.7-8.1235

H. Hotelling, Analysis of a complex of statistical variables into principal components., Journal of Educational Psychology, vol.24, issue.6, pp.417-428, 1933.
DOI : 10.1037/h0071325

. Hu, Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.9, pp.2117-2130, 2013.
DOI : 10.1109/TPAMI.2012.271

. Huang, Labeled faces in the wild: A database for studying face recognition in unconstrained environments, p.57, 2007.

L. Hunter, D. R. Hunter, and K. Lange, A tutorial on mm algorithms. The American Statistician, pp.30-37, 2004.

. Hwang, Learning a tree of metrics with disjoint visual features, Advances in neural information processing systems (NIPS, p.23, 2011.

. Hwang, Analogy-preserving semantic embedding for visual object categorization, International Conference on Machine Learning (ICML), 2013.

. Jatowt, Detecting age of page content, Proceedings of the 9th annual ACM international workshop on Web information and data management , WIDM '07, pp.137-144, 2007.
DOI : 10.1145/1316902.1316925

. Jégou, 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

T. Joachims, A support vector method for multivariate performance measures, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.377-384, 2005.
DOI : 10.1145/1102351.1102399

. Joachims, Cutting-plane training of structural SVMs, Machine Learning, pp.27-59, 2009.
DOI : 10.1007/s10994-009-5108-8

. Keerthi, . Decoste, S. S. Keerthi, and D. Decoste, A modified finite newton method for fast solution of large scale linear svms, Journal of Machine Learning Research, vol.6, issue.35, pp.341-88, 2005.

. Kohlschütter, Boilerplate detection using shallow text features, Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, p.67, 2010.
DOI : 10.1145/1718487.1718542

. Kovashka, Whittlesearch: Image search with relative attribute feedback, IEEE Conference on Computer Vision and Pattern Recognition (CVPR, p.12, 2012.
DOI : 10.1109/cvpr.2012.6248026

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.229.1134

. Krizhevsky, Imagenet classification with deep convolutional neural networks, Advances in neural information processing systems, pp.1097-1105, 2012.

J. B. Kruskal, Nonmetric multidimensional scaling: A numerical method, Psychometrika, vol.60, issue.2, pp.115-129, 1964.
DOI : 10.1007/BF02289694

B. Kulis, Metric Learning: A Survey, Machine Learning, pp.287-364, 2012.
DOI : 10.1561/2200000019

. Kumar, Attribute and simile classifiers for face verification, 2009 IEEE 12th International Conference on Computer Vision, p.60, 2009.
DOI : 10.1109/ICCV.2009.5459250

. Lajugie, Large margin metric learning for constrained partitioning problems, Proc. International Conference on Machine Learning, p.22, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00796921

. Lampert, Learning to detect unseen object classes by between-class attribute transfer, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.951-958, 2009.
DOI : 10.1109/CVPR.2009.5206594

. Law, Hybrid Pooling Fusion in the BoW Pipeline, Thome Proceedings of the 12th international conference on Computer Vision -Volume Part III, ECCV'12, pp.355-364, 2012.
DOI : 10.1007/978-3-642-33885-4_36

URL : https://hal.archives-ouvertes.fr/hal-01282471

. Law, Quadruplet-Wise Image Similarity Learning, 2013 IEEE International Conference on Computer Vision, p.65, 2013.
DOI : 10.1109/ICCV.2013.38

URL : https://hal.archives-ouvertes.fr/hal-01094069

. Law, Bag-of-Words Image Representation: Key Ideas and Further Insight, Fusion in Computer Vision, pp.29-52, 2014.
DOI : 10.1007/978-3-319-05696-8_2

URL : https://hal.archives-ouvertes.fr/hal-01221734

. Law, Fantope Regularization in Metric Learning, 2014 IEEE Conference on Computer Vision and Pattern Recognition, p.49, 2014.
DOI : 10.1109/CVPR.2014.138

URL : https://hal.archives-ouvertes.fr/hal-01094074

. Law, Structural and visual comparisons for web page archiving, Proceedings of the 2012 ACM symposium on Document engineering, DocEng '12, pp.71-79, 2012.
DOI : 10.1145/2361354.2361380

URL : https://hal.archives-ouvertes.fr/hal-01272800

. Lazebnik, 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), 2006.
DOI : 10.1109/CVPR.2006.68

URL : https://hal.archives-ouvertes.fr/inria-00548585

. Lecun, Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, vol.1, issue.4, pp.541-551, 1989.
DOI : 10.1007/BF00133697

. Lim, Robust structural metric learning, International Conference on Machine Learning (ICML). 4, p.50, 2013.

N. Lim, S. Lim, and Y. Ng, An automated change-detection algorithm for html documents based on semantic hierarchies, IEEE International Conference in Data Engineering (ICDE, p.66, 2001.

. Liu, -detecting and delivering information changes on the web, Proceedings of the ninth international conference on Information and knowledge management , CIKM '00, p.66, 2000.
DOI : 10.1145/354756.354860

URL : https://hal.archives-ouvertes.fr/tel-00259428

. Liu, In defense of soft-assignment coding, Computer Vision (ICCV), 2011 IEEE International Conference on, pp.2486-2493, 2011.

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

. Luo, Web article extraction for web printing, Proceedings of the 9th ACM symposium on Document engineering, DocEng '09, p.67, 2009.
DOI : 10.1145/1600193.1600208

M. Ma, W. Ma, and B. Manjunath, NeTra: A toolbox for navigating large image databases, ICIP. 1, 1997.
DOI : 10.1007/s005300050121

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.1078

. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, pp.281-297, 1967.

P. C. Mahalanobis, On the generalized distance in statistics, Proceedings of the National Institute of Sciences (Calcutta), pp.49-55, 1936.

M. Mahdavi, Exploiting smoothness in statistical learning, sequential prediction, and stochastic optimization. arXiv preprint arXiv:1407, p.17, 2014.

B. Mcfee and G. Lanckriet, Partial order embedding with multiple kernels, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.721-728, 2009.
DOI : 10.1145/1553374.1553467

B. Mcfee and G. Lanckriet, Metric learning to rank, International Conference on Machine Learning (ICML). 4, pp.22-63, 2010.

. Mensink, Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.11, pp.2624-2637, 2013.
DOI : 10.1109/TPAMI.2013.83

URL : https://hal.archives-ouvertes.fr/hal-00817211

. Mignon, . Jurie, A. Mignon, and F. Jurie, PCCA: A new approach for distance learning from sparse pairwise constraints, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.31-50, 2012.
DOI : 10.1109/CVPR.2012.6247987

URL : https://hal.archives-ouvertes.fr/hal-00806007

K. 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

A. M. Mood, Introduction to the theory of statistics, p.13, 1950.

B. K. Natarajan, Sparse Approximate Solutions to Linear Systems, SIAM Journal on Computing, vol.24, issue.2, pp.227-234, 1995.
DOI : 10.1137/S0097539792240406

. Ntoulas, What's new on the web?, Proceedings of the 13th conference on World Wide Web , WWW '04, p.67, 2004.
DOI : 10.1145/988672.988674

T. Oliva, A. Oliva, and A. Torralba, Modeling the shape of the scene: A holistic representation of the spatial envelope, International Journal of Computer Vision, vol.42, issue.3, pp.145-175, 2001.
DOI : 10.1023/A:1011139631724

. Oquab, 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

M. L. Overton and R. S. Womersley, On the Sum of the Largest Eigenvalues of a Symmetric Matrix, SIAM Journal on Matrix Analysis and Applications, vol.13, issue.1, pp.41-45, 1992.
DOI : 10.1137/0613006

W. Overton, M. L. Overton, and R. S. Womersley, Optimality conditions and duality theory for minimizing sums of the largest eigenvalues of symmetric matrices, Mathematical Programming, pp.1-3321, 1993.
DOI : 10.1007/BF01585173

G. Parikh, D. Parikh, and K. Grauman, Relative attributes, 2011 International Conference on Computer Vision, pp.31-39, 2011.
DOI : 10.1109/ICCV.2011.6126281

P. Parkash, A. Parkash, and D. Parikh, Attributes for Classifier Feedback, p.42, 2012.
DOI : 10.1007/978-3-642-33712-3_26

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.592.5907

. Pehlivan, Vi-DIFF: Understanding Web Pages Changes, Conference on Database and expert systems applications: Part I, p.67, 2010.
DOI : 10.1007/978-3-642-15364-8_1

URL : https://hal.archives-ouvertes.fr/hal-01292022

F. Perronnin and C. Dance, Fisher Kernels on Visual Vocabularies for Image Categorization, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383266

. Perronnin, Improving the Fisher Kernel for Large-Scale Image Classification, Computer Vision?ECCV 2010, pp.143-156, 2010.
DOI : 10.1007/978-3-642-15561-1_11

URL : https://hal.archives-ouvertes.fr/inria-00548630

L. Prechelt, Early Stopping ??? But When?, Neural Networks: Tricks of the trade, pp.55-69, 1998.
DOI : 10.1109/72.248452

. Rakotomamonjy, Penalty for sparse linear and sparse multiple kernel multitask learning, Neural Networks IEEE Transactions on, issue.8, pp.221307-1320, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00509608

. Rosasco, Are Loss Functions All the Same?, Neural Computation, vol.16, issue.5, pp.1063-1076, 2004.
DOI : 10.1006/jcom.2002.0635

. Rubner, The earth mover's distance as a metric for image retrieval, International Journal of Computer Vision, vol.40, issue.2, pp.99-121, 2000.
DOI : 10.1023/A:1026543900054

G. Salton, A theory of indexing, SIAM, vol.18, p.10, 1975.

G. Sanoja, A. Sanoja, and S. Gançarski, Yet another hybrid segmentation tool, International Conference on Preservation of Digital Objects. 71, p.80, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00770527

. Schölkopf, A Generalized Representer Theorem, Computational learning theory (COLT), pp.416-426, 2001.
DOI : 10.1007/3-540-44581-1_27

B. Scholkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond, 2001.

T. Joachims, Learning a distance metric from relative comparisons Advances in neural information processing systems, pp.41-66, 2004.

. Serre, Robust Object Recognition with Cortex-Like Mechanisms, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.3, pp.411-426, 2007.
DOI : 10.1109/TPAMI.2007.56

. Shalev-shwartz, Online and batch learning of pseudo-metrics, Twenty-first international conference on Machine learning , ICML '04, pp.94-108, 2004.
DOI : 10.1145/1015330.1015376

. Shaw, Learning a distance metric from a network, Advances in Neural Information Processing Systems (NIPS), pp.1899-1907, 2011.

. Shen, Positive semidefinite metric learning with boosting, Advances in neural information processing systems (NIPS, p.50, 2009.

. Shepard and R. N. Shepard, The analysis of proximities: Multidimensional scaling with an unknown distance function. I., Psychometrika, vol.65, issue.2, pp.125-140, 1962.
DOI : 10.1007/BF02289630

. Shepard and R. N. Shepard, The analysis of proximities: Multidimensional scaling with an unknown distance function. II, Psychometrika, vol.17, issue.3, pp.219-246, 1962.
DOI : 10.1007/BF02289621

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

Z. Sivic, J. Sivic, and A. Zisserman, Video Google: a text retrieval approach to object matching in videos, Proceedings Ninth IEEE International Conference on Computer Vision, p.45, 2003.
DOI : 10.1109/ICCV.2003.1238663

. Smeulders, Content-based image retrieval at the end of the early years, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.12, pp.221349-1380, 2000.
DOI : 10.1109/34.895972

. Smith, . Chang, J. R. Smith, and S. Chang, VisualSEEk, Proceedings of the fourth ACM international conference on Multimedia , MULTIMEDIA '96, pp.87-98, 1997.
DOI : 10.1145/244130.244151

. Song, Learning block importance models for web pages, Proceedings of the 13th conference on World Wide Web , WWW '04, p.70, 2004.
DOI : 10.1145/988672.988700

G. Spengler, A. Spengler, and P. Gallinari, Document structure meets page layout, Proceedings of the 10th ACM symposium on Document engineering, DocEng '10, p.67, 2010.
DOI : 10.1145/1860559.1860590

URL : https://hal.archives-ouvertes.fr/hal-00637719

H. Steinhaus, Sur la division des corp materiels en parties, Bull. Acad. Polon. Sci, vol.1, issue.10, pp.801-804, 1956.

O. Stricker, M. A. Stricker, and M. Orengo, Similarity of color images, IS&T/SPIE's Symposium on Electronic Imaging: Science & Technology International Society for Optics and Photonics, pp.381-392, 1995.

. Szegedy, Deep neural networks for object detection, Advances in Neural Information Processing Systems (NIPS), pp.2553-2561, 2013.

. Tenenbaum, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290, issue.5500, pp.2902319-2323, 2000.
DOI : 10.1126/science.290.5500.2319

. Tewari, . Bartlett, A. Tewari, and P. L. Bartlett, On the Consistency of Multiclass Classification Methods, The Journal of Machine Learning Research, vol.8, pp.1007-1025, 2007.
DOI : 10.1007/11503415_10

. Theriault, Dynamic Scene Classification: Learning Motion Descriptors with Slow Features Analysis, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.336

URL : https://hal.archives-ouvertes.fr/hal-01216646

. Tsochantaridis, Large margin methods for structured and interdependent output variables, Journal of Machine Learning Research, pp.1453-1484, 2005.

M. Tuytelaars, T. Tuytelaars, and K. Mikolajczyk, Local invariant feature detectors: a survey. Foundations and Trends® in Computer Graphics and Vision, pp.177-280, 2008.

A. Zisserman, A statistical approach to material classification using image patch exemplars. Pattern Analysis and Machine Intelligence, IEEE Transactions on, issue.11, pp.312032-2047, 2009.

. Verma, Learning hierarchical similarity metrics, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.24-44, 2012.
DOI : 10.1109/CVPR.2012.6247938

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.8108

. Wang, Localityconstrained linear coding for image classification, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3360-3367, 2010.

. Weinberger, . Chapelle, K. Weinberger, and O. Chapelle, Large margin taxonomy embedding with an application to document categorization, Advances in neural information processing systems (NIPS). 4, p.45, 2008.