S. Agarwal, A. Awan, and D. Roth, Learning to detect objects in images via a sparse, part-based representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.11, pp.1475-1490, 2004.
DOI : 10.1109/TPAMI.2004.108

J. Brank, M. Grobelnik, N. Milic-frayling, and D. Mladenic, Interaction of feature selection methods and linear classification models, Proceedings of the ICML Workshop on Text Learning, 2002.

G. Csurka, C. Dance, L. Fan, J. Williamowski, and C. Bray, Visual categorizationwith bags of keypoints, ECCV workshop on Statistical Learning in ComputerVision, pp.59-74, 2004.

G. Dorko and C. Schmid, Selection of scale-invariant parts for object class recognition, Proceedings Ninth IEEE International Conference on Computer Vision, pp.634-640, 2003.
DOI : 10.1109/ICCV.2003.1238407

URL : https://hal.archives-ouvertes.fr/inria-00548234

M. Everingham, L. V. Gool, C. Williams, and A. Zisserman, Pascal visual object classes challenge results, 2005.
DOI : 10.1007/11736790_8

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.6521

R. Fergus, P. Perona, and A. Zisserman, Object class recognition by unsupervised scale-invariant learning, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., pp.264-271, 2003.
DOI : 10.1109/CVPR.2003.1211479

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.7863

D. A. Forsyth and J. Ponce, Computer vision: A modern approach, 2003.
URL : https://hal.archives-ouvertes.fr/hal-01063327

D. Geman and A. Koloydenko, Invariant statistics and coding of natural microimages, IEEE Workshop on Statistical and Computational Theories of Vision, 1999.

B. Georgescu, I. Shimshoni, P. Meer, D. Hall, and J. L. Crowley, Mean shift based clustering in high dimensions: A texture classification example Detection du visage par caracteristiques generiques calculees a partir des images de luminance, ICCV Reconnaissance des Formes et Intelligence Artificiel, pp.456-463, 2003.

T. Hastie, R. Tibshirani, and J. Fridman, The Elements of Statistical Learning: Data Mining, Inference , and Prediction, 2001.

B. Heisele, P. Ho, J. Wu, and T. Poggio, Face recognition: component-based versus global approaches, Computer Vision and Image Understanding, vol.91, issue.1-2, pp.6-21, 2003.
DOI : 10.1016/S1077-3142(03)00073-0

T. Joachims, Text categorization with Support Vector Machines: Learning with many relevant features, Tenth European Conference on Machine Learning ECML-98, pp.137-142, 1999.
DOI : 10.1007/BFb0026683

F. Jurie and C. Schmid, Scale-invariant shape features for recognition of object categories, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., pp.90-96, 2004.
DOI : 10.1109/CVPR.2004.1315149

URL : https://hal.archives-ouvertes.fr/inria-00548545

F. Jurie and W. Triggs, Creating efficient codebooks for visual recognition, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005.
DOI : 10.1109/ICCV.2005.66

URL : https://hal.archives-ouvertes.fr/inria-00548511

B. Leibe and B. Schiele, Interleaved Object Categorization and Segmentation, Procedings of the British Machine Vision Conference 2003, 2003.
DOI : 10.5244/C.17.78

T. Leung and J. Malik, Representing and recognizing the visual appearance of materials using threedimensional textons, International Journal of Computer Vision, vol.43, issue.1, pp.29-44, 2001.
DOI : 10.1023/A:1011126920638

D. G. 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

R. R. Mettu and C. G. Plaxton, The online median problem, FOCS '00: Proceedings of the 41st Annual Symposium on Foundations of Computer Science, p.339, 2000.

K. Mikolajczyk and C. Schmid, An Affine Invariant Interest Point Detector, ECCV, 2002.
DOI : 10.1007/3-540-47969-4_9

URL : https://hal.archives-ouvertes.fr/inria-00548252

K. Mikolajczyk and C. Schmid, A performance evaluation of local descriptors, pp.257-263, 2003.
URL : https://hal.archives-ouvertes.fr/inria-00548227

V. N. Vapnik, The nature of statistical learning theory, 1995.

M. Vidal-naquet and S. Ullman, Object recognition with informative features and linear classification, Proceedings Ninth IEEE International Conference on Computer Vision, pp.281-288, 2003.
DOI : 10.1109/ICCV.2003.1238356

M. Weber, M. Welling, and P. Perona, Unsupervised Learning of Models for Recognition, ECCV, pages I, pp.18-32, 2000.
DOI : 10.1007/3-540-45054-8_2

M. Gary and . Weiss, Mining with rarity: a unifying framework, SIGKDD Explor. Newsl, vol.6, issue.1, pp.7-19, 2004.

S. C. Zhu, C. E. Guo, Y. Wang, and Z. Xu, What are textons? IJCV, pp.121-143, 2005.