A. Agarwal and B. Triggs, Multilevel Image Coding with Hyperfeatures, International Journal of Computer Vision, vol.19, issue.5, pp.15-27, 2008.
DOI : 10.1007/s11263-007-0072-x
URL : https://hal.archives-ouvertes.fr/hal-00192599

T. André, A. Vercauteren, M. B. Perchant, A. M. Wallace, N. Buchner et al., Endomicroscopic image retrieval and classification using invariant visual features, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.346-349, 2009.
DOI : 10.1109/ISBI.2009.5193055

T. André, A. Vercauteren, M. B. Perchant, A. M. Wallace, N. Buchner et al., Introducing Space and Time in Local Feature-Based Endomicroscopic Image Retrieval, Proceedings of the MICCAI 2009 Workshop -Medical Content-based Retrieval for Clinical Decision (MCBR-CDS'09), 2009.
DOI : 10.1007/978-3-642-11769-5_2

T. André, A. Vercauteren, M. B. Perchant, A. M. Wallace, N. Buchner et al., Endomicroscopic video retrieval using mosaicing and visual words, Proc. ISBI'10, 2010.

H. Bay, T. Tuytelaars, and L. J. Van-gool, SURF: Speeded Up Robust Features, Proc. ECCV'06, pp.404-417, 2006.

V. Becker, T. Vercauteren, C. H. Von-weyern, C. Prinz, R. M. Schmid et al., High-resolution miniprobe-based confocal microscopy in combination with video mosaicing (with video), Gastrointestinal Endoscopy, vol.66, issue.5, pp.1001-1007, 2007.
DOI : 10.1016/j.gie.2007.04.015
URL : https://hal.archives-ouvertes.fr/inria-00616022

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.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587598

A. M. Buchner, M. S. Ghabril, M. Krishna, H. C. Wolfsen, and M. B. Wallace, High-Resolution Confocal Endomicroscopy Probe System for In Vivo Diagnosis of Colorectal Neoplasia, Gastroenterology, vol.135, issue.1, p.295, 2008.
DOI : 10.1053/j.gastro.2008.05.063

A. M. Buchner, V. Gomez, K. R. Gill, M. Ghabril, D. Scimeca et al., The Learning Curve for In Vivo Probe Based Confocal Laser Endomicroscopy (pCLE) for Prediction of Colorectal Neoplasia, Gastrointestinal Endoscopy, vol.69, issue.5, pp.69-364, 2009.
DOI : 10.1016/j.gie.2009.03.1086

A. M. Buchner, M. W. Shahid, M. G. Heckman, M. Krishna, M. Ghabril et al., Comparison of Probe-Based Confocal Laser Endomicroscopy With Virtual Chromoendoscopy for Classification of Colon Polyps, Gastroenterology, vol.138, issue.3, 2009.
DOI : 10.1053/j.gastro.2009.10.053

X. Descombes, R. Morris, J. Zerubia, and M. Berthod, Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood, IEEE Transactions on Image Processing, vol.8, issue.7, pp.954-963, 1999.
DOI : 10.1109/83.772239
URL : https://hal.archives-ouvertes.fr/inria-00073679

A. Doyle, M. D. Madabhushi, J. E. Feldman, and . Tomaszeweski, A Boosting Cascade for Automated Detection of Prostate Cancer from Digitized Histology, Proc. MICCAI'06, pp.504-511, 2006.
DOI : 10.1007/11866763_62

M. Dundar, G. Fung, L. Bogoni, M. Macari, A. Megibow et al., A methodology for training and validating a CAD system and potential pitfalls, International Congress Series, vol.1268
DOI : 10.1016/j.ics.2003.10.002

A. M. Gomez, E. Buchner, F. J. Dekker, A. Van-den-broek, M. W. Meining et al., T1166 Interobserver Agreement and Accuracy Among International Experts of Probe-Based Confocal Laser Microscopy (pCLE) in Predicting Colorectal Neoplasia, Gastroenterology, vol.136, issue.5, 2010.
DOI : 10.1016/S0016-5085(09)62365-9

M. Häfner, A. Gangl, R. Kwitt, A. Uhl, A. Vécsei et al., Improving pit-pattern classification of endoscopy images by a combination of experts, Proc. MICCAI'09, pp.247-254, 2009.

R. M. Haralick, Statistical and structural approaches to texture, Proc. IEEE, pp.786-804, 1979.
DOI : 10.1109/PROC.1979.11328

H. Jegou, M. Douze, and C. Schmid, Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search, Proc. ECCV'08, pp.304-317, 2008.
DOI : 10.1007/978-3-540-88682-2_24
URL : https://hal.archives-ouvertes.fr/inria-00316866

J. Kong, O. Sertel, H. Shimada, K. L. Boyer, J. H. Saltz et al., Computer-aided evaluation of neuroblastoma on whole-slide histology images: Classifying grade of neuroblastic differentiation, Pattern Recognition, vol.42, issue.6, pp.1080-1092, 2009.
DOI : 10.1016/j.patcog.2008.10.035

L. Goualher, A. Perchant, M. Genet, C. Cavé, B. Viellerobe et al., Towards Optical Biopsies with an Integrated Fibered Confocal Fluorescence Microscope, Proc. MICCAI'04, pp.761-768, 2004.
DOI : 10.1007/978-3-540-30136-3_93
URL : https://hal.archives-ouvertes.fr/inria-00615981

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. 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

J. Matas, O. Chum, M. Urban, and T. Pajdla, Robust wide baseline stereo from maximally stable extremal regions, Proc. British Mach. Vision Conf, 2002.

K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas et al., A Comparison of Affine Region Detectors, International Journal of Computer Vision, vol.65, issue.1-2, pp.43-72, 2005.
DOI : 10.1007/s11263-005-3848-x
URL : https://hal.archives-ouvertes.fr/inria-00548528

H. Müller, N. Michoux, D. Bandon, and D. Geissbühler, A review of content-based image retrieval systems in medical applications???clinical benefits and future directions, International Journal of Medical Informatics, vol.73, issue.1, pp.1-23, 2004.
DOI : 10.1016/j.ijmedinf.2003.11.024

H. Müller, J. Kalpathy-cramer, C. E. Kahn, W. Hatt, S. Bedrick et al., Overview of the ImageCLEFmed 2008 Medical Image Retrieval Task, CLEF, pp.512-522, 2008.
DOI : 10.1007/3-540-45691-0_3

D. Nister and H. Stewenius, Scalable Recognition with a Vocabulary Tree, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.2161-2168, 2006.
DOI : 10.1109/CVPR.2006.264

O. Pele and M. Werman, Fast and robust Earth Mover's Distances, 2009 IEEE 12th International Conference on Computer Vision, 2009.
DOI : 10.1109/ICCV.2009.5459199

F. Perronnin and C. Dance, Fisher Kernels on Visual Vocabularies for Image Categorization, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383266

M. Petrou, R. Piroddi, and A. Talepbour, Texture recognition from sparsely and irregularly sampled data, Computer Vision and Image Understanding, vol.102, issue.1, pp.95-104, 2006.
DOI : 10.1016/j.cviu.2005.11.003

H. Pohl, T. Rosch, M. Vieth, M. Koch, V. Becker et al., Miniprobe confocal laser microscopy for the detection of invisible neoplasia in patients with Barrett's esophagus, Gut, issue.12, pp.571648-1653, 2008.

Y. Rubner, C. Tomasi, and L. J. Guibas, The Earth Mover's Distance as a metric for image retrieval, International Journal of Computer Vision, vol.40, issue.2, pp.99-121, 2000.
DOI : 10.1023/A:1026543900054

J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures, 2004.
DOI : 10.1201/9781420036268

J. Shotton, J. M. Winn, C. Rother, and A. Criminisi, TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation, Proc. ECCV'06, pp.1-15, 2006.
DOI : 10.1007/11744023_1

J. Sivic and A. Zisserman, Video Google: Efficient Visual Search of Videos, Toward Category-Level Object Recognition, pp.127-144, 2006.
DOI : 10.1007/11957959_7

J. Sivic and A. Zisserman, Efficient Visual Search of Videos Cast as Text Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.4, pp.591-606, 2009.
DOI : 10.1109/TPAMI.2008.111

A. W. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, Content-based image retrieval at the end of the early years, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.12, pp.1349-1380, 2000.
DOI : 10.1109/34.895972

S. Srivastava, J. J. Rodriguez, A. R. Rouse, M. A. Brewer, and A. F. Gmitro, Computer-aided identification of ovarian cancer in confocal microendoscope images, Journal of Biomedical Optics, vol.13, issue.2, p.24021, 2008.
DOI : 10.1117/1.2907167

T. Tuytelaars and L. J. Van-gool, Wide Baseline Stereo Matching based on Local, Affinely Invariant Regions, Procedings of the British Machine Vision Conference 2000, 2000.
DOI : 10.5244/C.14.38

T. Vercauteren, A. Perchant, G. Malandain, X. Pennec, and N. Ayache, Robust mosaicing with correction of motion distortions and tissue deformations for in vivo fibered microscopy, Medical Image Analysis, vol.10, issue.5, pp.673-692, 2006.
DOI : 10.1016/j.media.2006.06.006

M. B. Wallace and P. Fockens, Probe-Based Confocal Laser Endomicroscopy, Gastroenterology, vol.136, issue.5, pp.1509-1513, 2009.
DOI : 10.1053/j.gastro.2009.03.034
URL : https://hal.archives-ouvertes.fr/hal-00813813

H. Wang, M. M. Ullah, A. Kläser, I. Laptev, and C. Schmid, Evaluation of local spatio-temporal features for action recognition, Procedings of the British Machine Vision Conference 2009, p.127, 2009.
DOI : 10.5244/C.23.124
URL : https://hal.archives-ouvertes.fr/inria-00439769

J. Zhang, S. Lazebnik, and C. Schmid, Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study, International Journal of Computer Vision, vol.36, issue.1, pp.213-238, 2007.
DOI : 10.1007/s11263-006-9794-4
URL : https://hal.archives-ouvertes.fr/inria-00548574

S. Zhang, Q. Tian, G. Hua, Q. Huang, and S. Li, Descriptive visual words and visual phrases for image applications, Proceedings of the seventeen ACM international conference on Multimedia, MM '09, pp.75-84, 2009.
DOI : 10.1145/1631272.1631285

. Fig, The 10 most similar pCLE video sub-sequences (right) for a neoplastic query (left), retrieved by the LOPO Weighted-ImOfMos method, B indicates Benign, vol.24

. Fig, The 10 most similar pCLE video sub-sequences (right) for a neoplastic query (left), retrieved by the LOPO Weighted-ImOfMos method