S. Agarwal and D. Roth, Learning a Sparse Representation for Object Detection, Proc. ECCV 2002, pp.113-130
DOI : 10.1007/3-540-47979-1_8

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), 2005.
DOI : 10.1109/CVPR.2005.320

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.72.8044

A. Berger, S. D. Pietra, and V. D. Pietra, A Maximum Entropy Approach to Natural Language Processing, Computational Linguistics, vol.22, issue.1, pp.39-71, 1996.

P. Brodatz, Textures: A Photographic Album for Artists and Designers, 1966.

S. Chen and J. Goodman, An Empirical Study of Smoothing Techniques for Language Modeling, Proc. Conf. of the Association for Computational Linguistics, pp.310-318, 1996.

G. Csurka, C. Bray, C. Dance, and L. Fan, Visual Categorization with Bags of Keypoints, ECCV Workshop on Statistical Learning in Computer Vision, 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
DOI : 10.1109/ICCV.2003.1238407

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

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
DOI : 10.1109/CVPR.2003.1211479

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

V. Ferrari, T. Tuytelaars, and L. Van-gool, Simultaneous object recognition and segmentation by image exploration, Proc. ECCV, 2004.
DOI : 10.1007/11957959_8

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.3327

J. Jeon and R. Manmatha, Using Maximum Entropy for Automatic Image Annotation, Proc. Conf. on Image and Video Retrieval, pp.24-32, 2004.
DOI : 10.1007/978-3-540-27814-6_7

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., 2004.
DOI : 10.1109/CVPR.2004.1315149

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

D. Keysers, F. Och, and H. Ney, Maximum Entropy and Gaussian Models for Image Object Recognition, DAGM Symposium for Pattern Recognition, 2002.
DOI : 10.1007/3-540-45783-6_60

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

S. Lazebnik, C. Schmid, and J. Ponce, A maximum entropy framework for part-based texture and object recognition, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
DOI : 10.1109/ICCV.2005.10

URL : https://hal.archives-ouvertes.fr/inria-00548510

S. Lazebnik, C. Schmid, and J. Ponce, Semi-Local Affine Parts for Object Recognition, Procedings of the British Machine Vision Conference 2004, 2004.
DOI : 10.5244/C.18.98

URL : https://hal.archives-ouvertes.fr/inria-00548542

T. Lindeberg, Feature Detection with Automatic Scale Selection, pp.77-116, 1998.

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

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.4931

S. Mahamud, M. Hebert, and J. Lafferty, Combining Simple Discriminators for Object Discrimination, 2002.
DOI : 10.1007/3-540-47977-5_51

A. Mccallum and K. Nigam, A Comparison of Event Models for Naive Bayes Text Classification, AAAI-98 Workshop on Learning for Text Categorization, pp.41-48, 1998.

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

K. Nigam, J. Lafferty, and A. Mccallum, Using Maximum Entropy for Text Classification, IJCAI Workshop on Machine Learning for Information Filtering, pp.61-67, 1999.

F. Rothganger, S. Lazebnik, C. Schmid, and J. Ponce, 3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints, International Journal of Computer Vision, vol.17, issue.5, 2005.
DOI : 10.1007/s11263-005-3674-1

URL : https://hal.archives-ouvertes.fr/inria-00548618

J. Sivic and A. Zisserman, Video Google: a text retrieval approach to object matching in videos, Proceedings Ninth IEEE International Conference on Computer Vision, pp.1470-1477
DOI : 10.1109/ICCV.2003.1238663

J. Sivic, B. Russell, A. Efros, A. Zisserman, and W. Freeman, Discovering objects and their location in images, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
DOI : 10.1109/ICCV.2005.77

M. Varma and A. Zisserman, Texture classification: are filter banks necessary?, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., pp.691-698
DOI : 10.1109/CVPR.2003.1211534

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.158.5161

M. Weber, M. Welling, and P. Perona, Unsupervised Learning of Models for Recognition, Proc. ECCV, 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, International Workshop on Learning for Adaptable Visual Systems, 2004.

S. C. Zhu, Y. N. Wu, and D. Mumford, Filters, Random Fields, and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling, pp.1-20, 1998.