C. Bahlmann, B. Haasdonk, and H. Burkhardt, On-line handwriting recognition with support vector machines, a kernel approach, pp.49-54, 2002.

A. Barla, F. Odone, and A. Verri, Hausdorff Kernel for 3D Object Acquisition and Detection, Proceedings of the European conference on Computer vision LNCS 2353, pp.20-33, 2002.
DOI : 10.1007/3-540-47979-1_2

S. Belongie, J. Malik, and J. Puzicha, Shape context: A new descriptor for shape matching and object recognition, 2000.

A. Berg, T. Berg, and J. Malik, Shape Matching and Object Recognition Using Low Distortion Correspondences, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.26-33, 2005.
DOI : 10.1109/CVPR.2005.320

B. Boser, I. Guyon, and V. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992.
DOI : 10.1145/130385.130401

S. Boughorbel, Kernels for Image Classification with Support Vector Machines, 2005.

O. Chapelle, P. Haffner, and V. Vapnik, Svms for histogram-based image classification, Transaction on Neural Networks, vol.10, issue.5, 1999.

M. Cuturi, Etude de noyaux de semigroupe pour objets structures dans le cadre de l'apprentissage statistique, 2005.
URL : https://hal.archives-ouvertes.fr/pastel-00001823

T. Gartner, A survey of kernels for structured data, ACM SIGKDD Explorations Newsletter, vol.5, issue.1, pp.49-58, 2003.
DOI : 10.1145/959242.959248

G. Genton, Classes of kernels for machine learning: A statistics perspective, JMLR, vol.2, issue.12, pp.299-312, 2001.

K. Grauman and T. Darrell, The pyramid match kernel: Efficient learning with sets of features, Alvey Vision Conference, pp.725-760, 1988.

D. Haussler, Convolution kernels on discrete structures, 1999.

T. Jaakkola, M. Diekhans, and D. Haussler, Using the fisher kernel method to detect remote protein homologies, ISMB, pp.149-158, 1999.

R. Kondor and T. Jebara, A kernel between sets of vectors, proceedings of the 20th ICML conference, 2003.

S. Lafon, Y. Keller, and R. R. Coifman, Data fusion and multi-cue data matching by diffusion maps, IEEE Trans. Pattern Anal. Mach. Intell, issue.11, pp.281784-1797, 2006.

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

S. Lyu, Mercer kernels for object recognition with local features, the proceedings of the IEEE CVPR conference, 2005.

H. Miyao, M. Maruyama, Y. Nakano, and T. Hananoi, Off-line handwritten character recognition by SVM based on the virtual examples synthesized from on-line characters, Eighth International Conference on Document Analysis and Recognition (ICDAR'05), pp.494-498, 2005.
DOI : 10.1109/ICDAR.2005.170

P. Moreno, P. Ho, and N. Vasconcelos, A kullback-leibler divergence based kernel for svm classfication in multimedia applications, Neural Information Processing Systems, 2003.

J. Ng and S. Gong, Multi-view face detection and pose estimation using a composite support vector machine across the view sphere, Proceedings International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems. In Conjunction with ICCV'99 (Cat. No.PR00378), 1999.
DOI : 10.1109/RATFG.1999.799218

J. Nie, M. Simard, P. Isabelle, and R. Durand, Cross-language information retrieval based on parallel texts and automatic mining of parallel texts from the Web, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '99, 1999.
DOI : 10.1145/312624.312656

H. Sahbi, J. Audibert, J. Rabarisoa, and R. Keriven, Context-dependent kernel design for object matching and recognition, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587607
URL : https://hal.archives-ouvertes.fr/hal-00835081

C. Schmid and R. Mohr, Local grayvalue invariants for image retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.5, pp.530-535, 1997.
DOI : 10.1109/34.589215
URL : https://hal.archives-ouvertes.fr/inria-00548358

B. Scholkopf and A. Smola, Learning with kernels: Support vector machines, regularization, optimization and beyond, 2002.

B. Scholkopf, K. Tsuda, and J. Vert, Kernel methods in computational biology, 2004.

A. Shashua and T. Hazan, Algebraic set kernels with application to inference over local image representations, Neural Information Processing Systems (NIPS), 2004.

J. Shawe-taylor and N. Cristianini, Support vector machines and other kernel-based learning methods, 2000.

E. Shechtman and M. Irani, Matching Local Self-Similarities across Images and Videos, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383198
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.76.1297

K. Sim, W. Byrne, M. Gales, H. Sahbi, and P. Woodland, Consensus Network Decoding for Statistical Machine Translation System Combination, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, 2007.
DOI : 10.1109/ICASSP.2007.367174

M. Swain and D. Ballard, Color indexing, International Journal of Computer Vision, vol.31, issue.1, pp.11-32, 1991.
DOI : 10.1007/BF00130487

S. Tong and E. Chang, Support vector machine active learning for image retrieval, Proceedings of the ninth ACM international conference on Multimedia , MULTIMEDIA '01, pp.107-118, 2001.
DOI : 10.1145/500141.500159

V. N. Vapnik, Statistical learning theory, 1998.

C. Wallraven, B. Caputo, and A. Graf, Recognition with local features: the kernel recipe, Proceedings Ninth IEEE International Conference on Computer Vision, pp.257-264, 2003.
DOI : 10.1109/ICCV.2003.1238351

L. Wolf and A. Shashua, Learning over sets using kernel principal angles, Journal of Machine Learning Research, vol.4, pp.913-931, 2003.