.. De-tri-occurrences, 81 5.2.1. Attributs de tri-occurrences spatiaux Attributs de tri-occurrences spatiaux et spectraux, p.93

.. Et-du-couvert-paysager, Application à l'identification de plantes invasives, p.104

M. Hauta-kasari, J. Parkkinen, T. Jaaskelainen, and R. Lenz, Generalized cooccurrence matrix for multispectral texture analysis, Proceedings of the 13th International Conference on Pattern Recognition, pp.785-789, 1996.

V. Arvis, C. Debain, M. Berducat, and A. Benassi, GENERALIZATION OF THE COOCCURRENCE MATRIX FOR COLOUR IMAGES: APPLICATION TO COLOUR TEXTURE CLASSIFICATION, Image Analysis & Stereology, vol.23, issue.1, pp.63-72, 2011.
DOI : 10.5566/ias.v23.p63-72

F. Tsai, C. Chang, J. Rau, T. Lin, and G. Liu, 3D Computation of Gray Level Co-occurrence in Hyperspectral Image Cubes, Energy Minimization Methods in Computer Vision and Pattern Recognition, pp.429-440, 2007.
DOI : 10.1007/978-3-540-74198-5_33

R. Khelifi, M. Adel, and S. Bourennane, Multispectral texture characterization: application to computer aided diagnosis on prostatic tissue images, EURASIP Journal on Advances in Signal Processing, vol.2012, issue.1, pp.1-13, 2012.
DOI : 10.1109/LGRS.2005.857031

B. J. Frey and D. Dueck, Clustering by Passing Messages Between Data Points, Science, vol.315, issue.5814, pp.972-976, 2007.
DOI : 10.1126/science.1136800

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967.

P. J. Groenen and K. Jajuga, Fuzzy clustering with squared Minkowski distances, Fuzzy Sets and Systems, vol.120, issue.2, pp.227-237, 2001.
DOI : 10.1016/S0165-0114(98)00403-5

G. H. Ball and D. J. Hall, A clustering technique for summarizing multivariate data, Behavioral Science, vol.27, issue.2, pp.153-155, 1967.
DOI : 10.1002/bs.3830120210

S. Jia, Y. Qian, and Z. Ji, Band Selection for Hyperspectral Imagery Using Affinity Propagation, 2008 Digital Image Computing: Techniques and Applications, pp.137-141, 2008.
DOI : 10.1109/DICTA.2008.42

S. Jia, Z. Ji, Y. Qian, and L. Shen, Unsupervised Band Selection for Hyperspectral Imagery Classification Without Manual Band Removal, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.5, issue.2, pp.531-543, 2012.
DOI : 10.1109/JSTARS.2012.2187434

M. Soltani, K. Chehdi, and C. Cariou, Affinity propagation for large size hyperspectral image classification, Image and Signal Processing for Remote Sensing XIX, pp.88920-88920, 2013.
DOI : 10.1117/12.2028870

URL : https://hal.archives-ouvertes.fr/hal-00959550

K. Chehdi, M. Soltani, and C. Cariou, Pixel classification of large-size hyperspectral images by affinity propagation, Journal of Applied Remote Sensing, vol.8, issue.1, pp.83567-083567, 2014.
DOI : 10.1117/1.JRS.8.083567

URL : https://hal.archives-ouvertes.fr/hal-01123981

F. A. Kruse, Identification and mapping of minerals in drill core using hyperspectral image analysis of infrared reflectance spectra, International Journal of Remote Sensing, vol.17, issue.9, pp.1623-1632, 1996.
DOI : 10.1029/JB079i011p01615

C. Chang, Hyperspectral Data Exploitation: Theory and Applications, 2007.
DOI : 10.1002/0470124628

J. J. Workman, Review of Process and Non-invasive Near-Infrared and Infrared Spectroscopy: 1993???1999, Applied Spectroscopy Reviews, vol.122, issue.1-2, pp.1-89, 1993.
DOI : 10.1081/ASR-100100839

C. P. Warren, D. Even, W. Pfister, K. Nakanishi, A. Velasco et al., Miniaturized visible near-infrared hyperspectral imager for remotesensing applications, Optical Engineering, vol.51, issue.11, pp.111720-111721, 2012.

G. Lu and B. Fei, Medical hyperspectral imaging: a review, Journal of Biomedical Optics, vol.19, issue.1, p.10901, 2014.
DOI : 10.1117/1.JBO.19.1.010901

Q. Li, W. Wang, C. Ma, and Z. Zhu, Detection of physical defects in solar cells by hyperspectral imaging technology, Optics & Laser Technology, vol.42, issue.6, pp.1010-1013, 2010.
DOI : 10.1016/j.optlastec.2010.01.022

V. Farley, M. Chamberland, P. Lagueux, A. Vallières, A. Villemaire et al., Chemical agent detection and identification with a hyperspectral imaging infrared sensor, Proc. SPIE 6661, pp.66610-66610, 2007.

G. Elmasry, M. Kamruzzaman, D. Sun, and P. Allen, Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review, Critical Reviews in Food Science and Nutrition, vol.3, issue.1, pp.999-1023, 2012.
DOI : 10.1016/j.tifs.2006.06.005

. Pierna, Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review, Applied Spectroscopy Reviews, vol.48, issue.2, pp.142-159, 2013.

F. Vagni, Survey of Hyperspectral and Multispectral Imaging Technologies, 2007.

G. Hughes, On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory, vol.14, issue.1, pp.55-63, 1968.
DOI : 10.1109/TIT.1968.1054102

V. N. Vapnik, An overview of statistical learning theory, IEEE Transactions on Neural Networks, vol.10, issue.5, pp.988-999, 1999.
DOI : 10.1109/72.788640

S. Tadjudin and D. Landgrebe, Classification of high dimensional data with limited training samples, ECE Technical Reports, 1998.

R. Zwiggelaar, A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops, Crop Protection, vol.17, issue.3, pp.189-206, 1998.
DOI : 10.1016/S0261-2194(98)00009-X

R. A. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-188, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

A. P. Dempster, N. M. Laird, and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B (Methodological), vol.39, issue.1, pp.1-38, 1977.

C. M. Bishop, Neural networks for pattern recognition, 1995.

N. Cristianini and J. Shawe-taylor, An introduction to support vector machines: and other kernel-based learning methods. Cambridge, 2000.
DOI : 10.1017/CBO9780511801389

P. Chaovalit and L. Zhou, Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches, Proceedings of the 38th Annual Hawaii International Conference on System Sciences, pp.1-9, 2005.
DOI : 10.1109/HICSS.2005.445

J. Morlet, Sampling Theory and Wave Propagation Issues in Acoustic Signal ? Image Processing and Recognition, pp.233-261, 1983.

M. M. Galloway, Texture analysis using gray level run lengths, Computer Graphics and Image Processing, vol.4, issue.2, pp.172-179, 1975.
DOI : 10.1016/S0146-664X(75)80008-6

R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.610-621, 1973.
DOI : 10.1109/TSMC.1973.4309314

L. Soh and C. Tsatsoulis, Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices, IEEE Transactions on Geoscience and Remote Sensing, vol.37, issue.2, pp.780-795, 1999.
DOI : 10.1109/36.752194

M. N. Do and M. Vetterli, Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance, IEEE Transactions on Image Processing, vol.11, issue.2, pp.146-158, 2002.
DOI : 10.1109/83.982822

C. Rosenberger, Mise en oeuvre d'un système adaptatif de segmentation d'images, Thèse de Doctorat, 1999.

N. Voisine, Approche adaptative de coopération hiérarchique de méthodes de segmentation : application aux images multicomposantes, Thèse de Doctorat, 2002.

C. Rosenberger and K. Chehdi, Toward a complete adaptive analysis of an image, Journal of Electronic Imaging, vol.12, issue.2, pp.292-298, 2003.

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

R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.610-621, 1973.
DOI : 10.1109/TSMC.1973.4309314

R. M. Haralick, Statistical and structural approaches to texture, Proceedings of the IEEE, vol.67, issue.5, pp.786-804, 1979.
DOI : 10.1109/PROC.1979.11328

J. P. Cocquerez and S. Philipp-foliguet, Analyse d'images: filtrage et segmentation, 1995.
URL : https://hal.archives-ouvertes.fr/hal-00706168

S. Arivazhagan and L. Ganesan, Texture segmentation using wavelet transform, Pattern Recognition Letters, vol.24, issue.16, pp.3197-3203, 2003.
DOI : 10.1016/j.patrec.2003.08.005

S. Arivazhagan and L. Ganesan, Texture classification using wavelet transform, Pattern Recognition Letters, vol.24, issue.9-10, pp.1513-1521, 2003.
DOI : 10.1016/S0167-8655(02)00390-2

S. G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.7, pp.674-693, 1989.
DOI : 10.1109/34.192463

T. Aydin, Y. Yemez, E. Anarim, and B. Sankur, Multidirectional and multiscale edge detection via M-band wavelet transform, IEEE Transactions on Image Processing, vol.5, issue.9, pp.1370-1377, 1996.
DOI : 10.1109/83.535850

C. S. Lu, P. C. Chung, and C. F. Chen, Unsupervised texture segmentation via wavelet transform, Pattern Recognition, vol.30, issue.5, pp.729-742, 1997.
DOI : 10.1016/S0031-3203(96)00116-1

Y. Meyer, Ondelettes, filtres miroirs en quadrature et traitement numerique de l'image, 1990.
DOI : 10.1109/TASSP.1986.1164962

F. Truchetet, Ondelettes pour le signal numérique, 1998.

A. Mojsilovic, M. V. Popovic, and D. M. Rackov, On the selection of an optimal wavelet basis for texture characterization, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269), pp.2043-2050, 2000.
DOI : 10.1109/ICIP.1998.727351

A. Laine and J. Fan, Texture classification by wavelet packet signatures, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.15, issue.11, pp.1186-1191, 1993.
DOI : 10.1109/34.244679

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.203.6022

T. Chang and C. J. Kuo, Texture analysis and classification with tree-structured wavelet transform, IEEE Transactions on Image Processing, vol.2, issue.4, pp.429-441, 1993.
DOI : 10.1109/83.242353

S. Livens, P. Scheunders, G. V. De-wouwer, D. V. Dyck, H. Smets et al., A Texture Analysis Approach to Corrosion Image Classification, Microscopy Microanalysis Microstructures, vol.7, issue.2
DOI : 10.1051/mmm:1996110

M. Simard, S. S. Saatchi, and G. Grandi, The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest, IEEE Transactions on Geoscience and Remote Sensing, vol.38, issue.5, pp.2310-2321, 2000.
DOI : 10.1109/36.868888

A. Gagalowicz, A New Method for Texture Fields Synthesis: Some Applications to the Study of Human Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.3, issue.5, pp.520-533, 1981.
DOI : 10.1109/TPAMI.1981.4767145

A. Gagalowicz, Vers un modèle de textures, Thèse de Doctorat, 1983.

A. B. Reuze, Caractérisation d'images texturées basée sur les statistiques d'ordre trois, 15° Colloque Gretsi, pp.645-648, 1995.

C. Coroyer, Apport des corrélations d'ordre élevé à l'analyse de textures nongaussiennes , Colloque Gretsi, pp.129-138, 1998.

H. R. Kalluri, S. Prasad, and L. M. Bruce, Decision-Level Fusion of Spectral Reflectance and Derivative Information for Robust Hyperspectral Land Cover Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.11, pp.4047-4058, 2010.
DOI : 10.1109/TGRS.2010.2072787

J. C. Harsanyi and C. Chang, Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach, IEEE Transactions on Geoscience and Remote Sensing, vol.32, issue.4, pp.779-785, 1994.
DOI : 10.1109/36.298007

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.5825

C. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, 2003.
DOI : 10.1007/978-1-4419-9170-6

C. Münzenmayer, H. Volk, C. Küblbeck, K. Spinnler, and T. Wittenberg, Multispectral Texture Analysis Using Interplane Sum- and Difference-Histograms, pp.42-49, 2002.
DOI : 10.1007/3-540-45783-6_6

J. A. Benediktsson, M. Pesaresi, and K. Amason, Classification and feature extraction for remote sensing images from urban areas based on morphological transformations, IEEE Transactions on Geoscience and Remote Sensing, vol.41, issue.9, pp.1940-1949, 2003.
DOI : 10.1109/TGRS.2003.814625

R. Kondepudy and G. Healey, Use of invariants for recognition of three-dimensional color textures, Journal of the Optical Society of America A, vol.11, issue.11, pp.3037-3049, 1994.
DOI : 10.1364/JOSAA.11.003037

O. Rajadell, P. García-sevilla, and F. Pla, Textural Features for Hyperspectral Pixel Classification, Pattern Recognition and Image Analysis, pp.208-216, 2009.
DOI : 10.1007/978-3-642-02172-5_28

G. Rellier, X. Descombes, F. Falzon, and J. Zerubia, Texture feature analysis using a gauss-Markov model in hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.7, pp.1543-1551, 2004.
DOI : 10.1109/TGRS.2004.830170

S. Sarkar and G. Healey, Hyperspectral texture classification using generalized Markov fields, Optical Engineering, vol.43, issue.12, pp.3038-3044, 2004.
DOI : 10.1117/1.1811083

L. Lepisto, I. Kunttu, J. Autio, and A. Visa, Classification method for colored natural textures using Gabor filtering, 12th International Conference on Image Analysis and Processing, 2003.Proceedings., pp.397-401, 2003.
DOI : 10.1109/ICIAP.2003.1234082

M. Hauta-kasari, J. Parkkinen, T. Jaaskelainen, and R. Lenz, Generalized cooccurrence matrix for multispectral texture analysis, Proceedings of the 13th International Conference on Pattern Recognition, pp.785-789, 1996.

R. Khelifi, M. Adel, and S. Bourennane, Texture classification for multi-spectral images using spatial and spectral Gray Level Differences, 2010 2nd International Conference on Image Processing Theory, Tools and Applications, pp.330-333, 2010.
DOI : 10.1109/IPTA.2010.5586795

URL : https://hal.archives-ouvertes.fr/hal-00486931

R. Khelifi, M. Adel, S. Bourennane, and A. Moussaoui, Generalized gray level dependence method for prostate cancer classification, International Workshop on Systems, Signal Processing and their Applications, WOSSPA, pp.295-298, 2011.
DOI : 10.1109/WOSSPA.2011.5931477

URL : https://hal.archives-ouvertes.fr/hal-01280687

L. Lepistö, J. Livari-kunttu, A. Autio, and . Visa, Rock Image Classification Using Non-Homogenous Textures and Spectral Imaging, Spectral Imaging, WSCG SHORT PAPERS proceedings, 2003.

M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.11, pp.3804-3814, 2008.
DOI : 10.1109/TGRS.2008.922034

URL : https://hal.archives-ouvertes.fr/hal-00178885

J. Palmason, J. A. Benediktsson, J. R. Sveinsson, and J. Chanussot, Classification of hyperspectral data from urban areas using morphological preprocessing and independent component analysis, IEEE International Proceedings of Geoscience and Remote Sensing Symposium, IGARSS, pp.4-25, 2005.

A. Plaza, P. Martinez, R. Perez, and J. Plaza, Spatial/spectral endmember extraction by multidimensional morphological operations, IEEE Transactions on Geoscience and Remote Sensing, vol.40, issue.9, pp.2025-2041, 2002.
DOI : 10.1109/TGRS.2002.802494

D. Mahmoud-ghoneim, G. Toussaint, J. M. Constans, and J. D. De-certaines, Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas, Magnetic Resonance Imaging, vol.21, issue.9, pp.983-987, 2003.
DOI : 10.1016/S0730-725X(03)00201-7

R. Khelifi, M. Adel, and S. Bourennane, Spatial and spectral dependance cooccurrence method for multi-spectral image texture classification, 17th IEEE International Conference on Image Processing (ICIP), pp.4361-4364, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00486752

T. Kohonen, The self-organizing map, Proceedings of the IEEE, vol.78, issue.9, pp.1464-1480, 1990.
DOI : 10.1109/5.58325

P. T. Kohonen, Self-Organizing Feature Maps, Self-Organization and Associative Memory, pp.119-157, 1989.

Y. Linde, A. Buzo, and R. M. Gray, An Algorithm for Vector Quantizer Design, IEEE Transactions on Communications, vol.28, issue.1, pp.84-95, 1980.
DOI : 10.1109/TCOM.1980.1094577

R. Krishnapuram and J. M. Keller, A possibilistic approach to clustering, IEEE Transactions on Fuzzy Systems, vol.1, issue.2, pp.98-110, 1993.
DOI : 10.1109/91.227387

D. S. Lin, A self-organizing semantic map for information retrieval, Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '91, pp.262-269, 1991.
DOI : 10.1145/122860.122887

X. Wu and V. Kumar, The Top Ten Algorithms in Data Mining, 2009.
DOI : 10.1201/9781420089653

Q. Wen and M. E. Celebi, Hard versus fuzzy c-means clustering for color quantization, EURASIP Journal on Advances in Signal Processing, vol.2011, issue.1, pp.1-12, 2011.
DOI : 10.1186/1687-6180-2011-118

J. A. Hartigan and M. A. Wong, Algorithm AS 136: A K-Means Clustering Algorithm, Applied Statistics, vol.28, issue.1, pp.100-108, 1979.
DOI : 10.2307/2346830

K. Alsabti, S. Ranka, and V. Singh, An efficient k-means clustering algorithm, IEEE Transactions On Pattern Analysis And Machine Intelligence, vol.24, issue.7, pp.881-892, 2002.

C. Elkan, Using the Triangle Inequality to Accelerate k-Means, Proceedings of the Twentieth International Conference on Machine Learning, pp.147-153, 2003.

M. Song and S. Rajasekaran, Fast k-Means Algorithms with Constant Approximation, Algorithms and Computation, pp.1029-1038, 2005.
DOI : 10.1007/11602613_102

G. Karypis, E. Han, and V. Kumar, Chameleon: hierarchical clustering using dynamic modeling, Computer, vol.32, issue.8, pp.68-75, 1999.
DOI : 10.1109/2.781637

M. Kunt, Reconnaissance des formes et analyse de scènes, Collection Electricité, 2000.

E. L. Bohez, Two level cluster analysis based on fractal dimension and iterated function systems (IFS) for speech signal recognition, IEEE. APCCAS 1998. 1998 IEEE Asia-Pacific Conference on Circuits and Systems. Microelectronics and Integrating Systems. Proceedings (Cat. No.98EX242), pp.291-294, 1998.
DOI : 10.1109/APCCAS.1998.743749

M. F. Hussin, M. S. Kamel, and M. H. Nagi, An Efficient Two-Level SOMART Document Clustering Through Dimensionality Reduction, Neural Information Processing, pp.158-165, 2004.
DOI : 10.1007/978-3-540-30499-9_23

Y. B. Guérif, Selection of clusters number and features subset during a twolevels clustering task, Proceeding of Artificial Intelligence and Soft Computing, pp.28-33, 2006.

L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, 2009.
DOI : 10.1002/9780470316801

M. Mitchell, An Introduction to Genetic Algorithms, 1998.

D. Pham and D. Karaboga, Intelligent Optimisation Techniques -Genetic Algorithms, Simulated Annealing and Neural Networks, 2010.
DOI : 10.1007/978-1-4471-0721-7

D. F. Rothlauf, Representations for Genetic and Evolutionary Algorithms, Representations for Genetic and Evolutionary Algorithms, pp.9-32, 2006.
DOI : 10.1007/978-3-642-88094-0

K. A. De and . Jong, An Analysis of the Behavior of a Class of Genetic Adaptive Systems, 1975.

S. Bandyopadhyay and U. Maulik, Nonparametric genetic clustering: comparison of validity indices, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.31, issue.1, pp.120-125, 2001.
DOI : 10.1109/5326.923275

T. J. Ross, Fuzzy Logic with Engineering Applications, 2009.
DOI : 10.1002/9781119994374

S. Bandyopadhyay and U. Maulik, Genetic clustering for automatic evolution of clusters and application to image classification, Pattern Recognition, vol.35, issue.6, pp.1197-1208, 2002.
DOI : 10.1016/S0031-3203(01)00108-X

C. Hung, S. Kulkarni, and B. Kuo, A New Weighted Fuzzy C-Means Clustering Algorithm for Remotely Sensed Image Classification, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.3, pp.543-553, 2011.
DOI : 10.1109/JSTSP.2010.2096797

F. Yao and Y. Qian, Band selection based gaussian processes for hyperspectral remote sensing images classification, 16th IEEE International Conference on Image Processing (ICIP), pp.2845-2848, 2009.

Y. Yang, H. Ha, F. C. Fleites, and S. Chen, A Multimedia Semantic Retrieval Mobile System Based on HCFGs, IEEE MultiMedia, vol.21, issue.1, pp.36-46, 2014.
DOI : 10.1109/MMUL.2013.33

H. Xiao and P. Guo, Iris Image Analysis Based on Affinity Propagation Algorithm Advances in Neural Networks, pp.943-949, 2009.

C. Wang, J. Lai, C. Y. Suen, and J. Zhu, Multi-Exemplar Affinity Propagation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.9, pp.2223-2237, 2013.
DOI : 10.1109/TPAMI.2013.28

Z. Liu, P. Li, Y. Zheng, and M. Sun, Clustering to find exemplar terms for keyphrase extraction, Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 1, EMNLP '09, pp.257-266, 2009.
DOI : 10.3115/1699510.1699544

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.187.7547

Z. Zha, L. Yang, T. Mei, M. Wang, and Z. Wang, Visual Query Suggestion, Proceedings of the 17th ACM International Conference on Multimedia, pp.15-24, 2009.
DOI : 10.1145/1823746.1823747

K. Lindorff-larsen and J. Ferkinghoff-borg, Similarity Measures for Protein Ensembles, PLoS ONE, vol.13, issue.1, pp.15-22, 2009.
DOI : 10.1371/journal.pone.0004203.g006

Y. Qian, F. Yao, and S. Jia, Band selection for hyperspectral imagery using affinity propagation, IET Computer Vision, vol.3, issue.4, pp.213-222, 2009.
DOI : 10.1049/iet-cvi.2009.0034

T. M. Cover and J. A. Thomas, Elements of Information Theory, 2012.

S. Jia, Y. Qian, J. Li, W. Liu, and Z. Ji, Feature extraction and selection hybrid algorithm for hyperspectral imagery classification, 2010 IEEE International Geoscience and Remote Sensing Symposium, pp.72-75, 2010.
DOI : 10.1109/IGARSS.2010.5652463

C. Yang, S. Liu, L. Bruzzone, R. Guan, and P. Du, A Feature-Metric-Based Affinity Propagation Technique for Feature Selection in Hyperspectral Image Classification

C. Yang, L. Bruzzone, F. Sun, L. Lu, R. Guan et al., A Fuzzy-Statistics-Based Affinity Propagation Technique for Clustering in Multispectral Images, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.6, pp.2647-2659, 2010.
DOI : 10.1109/TGRS.2010.2040035

C. Yang, L. Bruzzone, R. Guan, L. Lu, and Y. Liang, Incremental and Decremental Affinity Propagation for Semisupervised Clustering in Multispectral Images, IEEE Transactions on Geoscience and Remote Sensing, vol.51, issue.3, pp.1666-1679, 2013.
DOI : 10.1109/TGRS.2012.2206818

I. Givoni, C. Chung, and B. J. Frey, Hierarchical Affinity Propagation, Computer Science, pp.212-221, 2012.

R. Guan, X. Shi, M. Marchese, C. Yang, and Y. Liang, Text Clustering with Seeds Affinity Propagation, IEEE Transactions on Knowledge and Data Engineering, vol.23, issue.4, pp.627-637, 2011.
DOI : 10.1109/TKDE.2010.144

J. A. Cardille, J. C. White, M. A. Wulder, and T. Holland, Representative Landscapes in the Forested Area of Canada, Environmental Management, vol.27, issue.1, pp.163-173, 2012.
DOI : 10.1007/s00267-011-9785-2

M. Sharma, Performance evaluation of image segmentation and texture extraction methods in scene analysis Master of philosophy in computer Science to the university of Exter, 2001.

S. Ghandour, Segmentation d'images couleurs par morphologie mathématique : application aux images microscopiques, Thèse de doctorat, 2010.

J. Cocquerez and J. Devars, Edge detection in aerial pictures: new operator, pp.45-65, 1985.

X. Zhang and M. D. Desai, Segmentation of bright targets using wavelets and adaptive thresholding, IEEE Transactions on Image Processing, vol.10, issue.7, pp.1020-1030, 2001.
DOI : 10.1109/83.931096

L. Vinet, Segmentation et mise en correspondance de régions de paires d'images stéréoscopiques segmentation and region matching of a stereo pair of images, Thèse de Doctorat, 1991.

G. Cleuziou, Une méthode de classification non-supervisée pour l'apprentissage de règles et la recherche d'information, Thèse de Doctorat, 2004.

S. Sharma, Applied Multivariate Techniques, Thèse de Doctorat, 1995.

T. Cali?ski and J. Harabasz, A dendrite method for cluster analysis, Communications in Statistics, vol.3, issue.1, pp.1-27, 1974.

D. L. Davies and D. W. Bouldin, A Cluster Separation Measure, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.1, issue.2, pp.224-227, 1979.
DOI : 10.1109/TPAMI.1979.4766909

X. L. Xie and G. Beni, A validity measure for fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.8, pp.841-847, 1991.
DOI : 10.1109/34.85677

M. Halkidi, M. Vazirgiannis, and Y. Batistakis, Quality Scheme Assessment in the Clustering Process, Principles of Data Mining and Knowledge Discovery, pp.265-276, 2000.
DOI : 10.1007/3-540-45372-5_26

M. Halkidi and M. Vazirgiannis, Clustering validity assessment: finding the optimal partitioning of a data set, Proceedings 2001 IEEE International Conference on Data Mining, pp.187-194, 2001.
DOI : 10.1109/ICDM.2001.989517

P. J. Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, vol.20, pp.53-65, 1987.
DOI : 10.1016/0377-0427(87)90125-7

J. C. Dunn, Well-Separated Clusters and Optimal Fuzzy Partitions, Journal of Cybernetics, vol.4, issue.1, pp.95-104, 1974.
DOI : 10.1080/01969727408546059

M. Moghrani, Segmentation coopérative et adaptative d'images multicomposantes : application aux images CASI, Thèse de Doctorat, 2007.

K. C. Chehdi and . Kermad, Segmentation d'images par multi-seuillage et fusion de régions labellisées minimisant un critère de similarité, 15° Colloque sur le traitement du signal et des images, pp.641-644, 1995.

M. D. Levine and A. M. Nazif, Dynamic Measurement of Computer Generated Image Segmentations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.7, issue.2
DOI : 10.1109/TPAMI.1985.4767640

D. Dasgupta, Artificial Immune Systems and Their Applications, 1999.
DOI : 10.1007/978-3-642-59901-9

J. H. Carter, The Immune System as a Model for Pattern Recognition and Classification, Journal of the American Medical Informatics Association, vol.7, issue.1, pp.28-41, 2000.
DOI : 10.1136/jamia.2000.0070028

J. Timmis, M. Neal, and J. E. Hunt, An artificial immune system for data analysis, Biosystems, vol.55, issue.1-3, pp.143-150, 2000.
DOI : 10.1016/S0303-2647(99)00092-1

U. Maulik and S. Bandyopadhyay, Performance evaluation of some clustering algorithms and validity indices, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.12, pp.1650-1654, 2002.
DOI : 10.1109/TPAMI.2002.1114856

Z. Zhu, S. Jia, and Z. Ji, Towards a Memetic Feature Selection Paradigm [Application Notes, IEEE Computational Intelligence Magazine, vol.5, issue.2, pp.41-53, 2010.
DOI : 10.1109/MCI.2010.936311

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone et al., Recent advances in techniques for hyperspectral image processing, Remote Sensing of Environment, vol.113, pp.110-122, 2009.
DOI : 10.1016/j.rse.2007.07.028

URL : https://hal.archives-ouvertes.fr/hal-00178888

A. Singla and M. Karambir, Comparative Analysis & Evaluation of Euclidean Distance Function and Manhattan Distance Function Using K-means Algorithm, International Journal of Advanced Research in Computer Science and Software Engineering (IJARSSE), vol.2, issue.7, pp.298-300, 2012.

G. Khosla, N. Rajpal, and J. Singh, Evaluation of Euclidean and Manhanttan Metrics In Content Based Image Retrieval System, Journal of Engineering Research and Applications, vol.4, issue.9, pp.43-49, 2014.