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, ICML'02 Workshop on Text Learning, 2002.

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.
DOI : 10.1109/34.1000236

G. Csurka, C. Dance, L. Fan, J. Williamowski, and C. Bray, Visual categorization with bags of keypoints, ECCV'04 workshop on Statistical Learning in Computer Vision, pp.59-74, 2004.

D. Cuttin, D. Karger, J. Pedersen, and J. Tukey, Scatter/gather: A cluster-based approach to browsing large document collections, SIGR, pp.318-329, 1992.

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

A. Estabrooks, T. Jo, and N. Japkowicz, A Multiple Resampling Method for Learning from Imbalanced Data Sets, Computational Intelligence, vol.19, issue.3, pp.18-36, 2004.
DOI : 10.1109/78.668782

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

D. Geman, A. Koloydenko, ]. B. Georgescu, I. Shimshoni, and P. Meer, Invariant statistics and coding of natural microimages Mean shift based clustering in high dimensions: a texture classification example, IEEE Workshop on Statistical and Computational Theories of Vision ICCV, pp.456-463, 1999.

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

B. Julesz, Textons, the elements of texture perception, and their interactions, Nature, vol.32, issue.5802, pp.91-97, 1981.
DOI : 10.1038/290091a0

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

S. Lazebnik, C. Schmid, and J. Ponce, A sparse texture representation using local affine regions, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1265-1278, 2005.
DOI : 10.1109/TPAMI.2005.151

URL : https://hal.archives-ouvertes.fr/inria-00548530

A. Lee, D. Mumford, and J. Huang, Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model, International Journal of Computer Vision, vol.41, issue.1/2, pp.35-59, 2001.
DOI : 10.1023/A:1011109015675

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

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

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

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

A. Meyerson, Online facility location, Proceedings 2001 IEEE International Conference on Cluster Computing, pp.426-433, 2001.
DOI : 10.1109/SFCS.2001.959917

A. Meyerson, L. O. Callaghan, and S. Plotkin, A k-Median Algorithm with Running Time Independent of Data Size, Machine Learning, pp.1-361, 2004.
DOI : 10.1023/B:MACH.0000033115.78247.f0

K. Mikolajczyk and C. Schmid, An Affine Invariant Interest Point Detector, ECCV, p.128, 2002.
DOI : 10.1007/3-540-47969-4_9

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

D. Mladenic, J. Brank, M. Grobelnik, and N. , Milic-Frayling. Feature selection using linear classifier weights: Interaction with classification models, SIGIR, pp.234-241, 2004.

A. Torralba, K. Murphy, and W. Freeman, Sharing features: efficient boosting procedures for multiclass object detection, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., pp.762-769, 2004.
DOI : 10.1109/CVPR.2004.1315241

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

J. Willamowski, D. Arregui, G. Csurka, C. R. Dance, and L. Fan, Categorizing nine visual classes using local appearance descriptors, Workshop on Learning for Adaptable Visual Systems (LAVS04), 2004.

S. Zhu, C. Guo, Y. Wang, and Z. Xu, What are textons? IJCV, pp.121-143, 2005.
DOI : 10.1007/3-540-47979-1_53

G. Zipf, Selected Studies of the Principle of Relative Frequency in Language, 1932.