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

H. Zhang, A. C. Berg, M. Maire, and J. Malik, SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.6-2126, 2006.
DOI : 10.1109/CVPR.2006.301

O. Boiman, E. Shechtman, and M. Irani, In defense of Nearest-Neighbor based image classification, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.8-9, 2008.
DOI : 10.1109/CVPR.2008.4587598

T. Hastie and R. Tibshirani, Discriminant adaptive nearest neighbor classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.6, pp.607-616, 1996.
DOI : 10.1109/34.506411

R. Paredes, Learning weighted metrics to minimize nearest-neighbor classification error, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.7, pp.1100-1110, 2006.
DOI : 10.1109/TPAMI.2006.145

J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, Information-theoretic metric learning, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.209-216, 2007.
DOI : 10.1145/1273496.1273523

H. Brighton and C. Mellish, Advances in instance selection for instance-based learning algorithms . Data Mining and Knowledge Disc, pp.153-172, 2002.

W. Zuo, D. Zhang, and K. Wang, On kernel difference-weighted k-nearest neighbor classification, Pattern Analysis and Applications, vol.35, issue.6, pp.247-257, 2008.
DOI : 10.1007/s10044-007-0100-z

C. C. Holmes and N. M. Adams, Likelihood inference in nearest-neighbour classification models, Biometrika, vol.90, issue.1, pp.99-112, 2003.
DOI : 10.1093/biomet/90.1.99

J. M. Marin, C. P. Robert, and D. M. Titterington, A Bayesian reassessment of nearest-neighbor classification, J. of the Am. Stat. Assoc, issue.2, 2009.

V. Athitsos and S. Sclaroff, Boosting Nearest Neighbor Classi.ers for Multiclass Recognition, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), Workshops, p.3, 2005.
DOI : 10.1109/CVPR.2005.424

R. E. Schapire and Y. Singer, Improved boosting algorithms using confidence-rated predictions, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.297-336, 1999.
DOI : 10.1145/279943.279960

H. Zou, J. Zhu, and T. Hastie, New multicategory boosting algorithms based on multicategory Fisher-consistent losses, The Annals of Applied Statistics, vol.2, issue.4, pp.1290-1306, 2008.
DOI : 10.1214/08-AOAS198

R. Nock and F. Nielsen, Bregman Divergences and Surrogates for Learning, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.11, pp.2048-2059, 2009.
DOI : 10.1109/TPAMI.2008.225

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

Y. Freund and R. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.55-119, 1997.
DOI : 10.1006/jcss.1997.1504

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

S. Lazebnik, C. Schmid, and J. Ponce, 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), pp.2169-2178, 2006.
DOI : 10.1109/CVPR.2006.68
URL : https://hal.archives-ouvertes.fr/inria-00548585

J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, Lost in quantization: Improving particular object retrieval in large scale image databases, 2008 IEEE Conference on Computer Vision and Pattern Recognition, p.10, 2008.
DOI : 10.1109/CVPR.2008.4587635