A. Alzu-'bi, A. Amira, and N. Ramzan, Semantic content-based image retrieval: A comprehensive study, J. Vis. Commun. Image Represent, vol.32, pp.20-54, 2015.

T. Dharani and I. L. Aroquiaraj, A survey on content based image retrieval, 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering
DOI : 10.1109/ICPRIME.2013.6496719

R. Veltkamp, H. Burkhardt, and H. P. Kriegel, State-of-the-Art in Content-Based Image and Video Retrieval Wavelet-based texture retrieval using generalized Gaussian density and Kullback- Leibler distance, IEEE Trans. Image Process, vol.22, issue.11, pp.146-158, 2002.

M. Kokare, P. K. Biswas, and B. N. Chatterji, Texture Image Retrieval Using New Rotated Complex Wavelet Filters, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.35, issue.6, pp.1168-1178, 2005.
DOI : 10.1109/TSMCB.2005.850176

R. Kwitt and A. Uhl, Image similarity measurement by Kullback-Leibler divergences between complex wavelet subband statistics for texture retrieval, 2008 15th IEEE International Conference on Image Processing, pp.12-15, 2008.
DOI : 10.1109/ICIP.2008.4711909

R. Kwitt and A. Uhl, Lightweight Probabilistic Texture Retrieval, IEEE Transactions on Image Processing, vol.19, issue.1, pp.241-253
DOI : 10.1109/TIP.2009.2032313

S. K. Choy and C. S. Tong, Statistical Wavelet Subband Characterization Based on Generalized Gamma Density and Its Application in Texture Retrieval, IEEE Transactions on Image Processing, vol.19, issue.2, pp.281-289
DOI : 10.1109/TIP.2009.2033400

N. E. Lasmar and Y. Berthoumieu, Gaussian Copula Multivariate Modeling for Texture Image Retrieval Using Wavelet Transforms, IEEE Transactions on Image Processing, vol.23, issue.5, pp.2246-2261, 2014.
DOI : 10.1109/TIP.2014.2313232
URL : https://hal.archives-ouvertes.fr/hal-00727127

C. Li, Y. Huang, and L. Zhu, Color texture image retrieval based on Gaussian copula models of Gabor wavelets, Pattern Recognition, vol.64, pp.118-129, 2017.
DOI : 10.1016/j.patcog.2016.10.030

G. Verdoolaege, S. De-backer, and P. Scheunders, Multiscale colour texture retrieval using the geodesic distance between multivariate generalized Gaussian models, 2008 15th IEEE International Conference on Image Processing, pp.12-15, 2008.
DOI : 10.1109/ICIP.2008.4711718

R. Kwitt, P. Meerwald, and A. Uhl, Efficient Texture Image Retrieval Using Copulas in a Bayesian Framework, IEEE Transactions on Image Processing, vol.20, issue.7, pp.2063-2077, 2011.
DOI : 10.1109/TIP.2011.2108663

T. Ojala, M. Pietikäinen, and T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.7, pp.971-987, 2002.
DOI : 10.1109/TPAMI.2002.1017623
URL : http://www.ee.oulu.fi/research/imag/texture/publications/show_pdf.php?ID=94

B. Zhang, Y. Gao, S. Zhao, and J. Liu, Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor, IEEE Transactions on Image Processing, vol.19, issue.2, pp.533-544, 2010.
DOI : 10.1109/TIP.2009.2035882

M. Subrahmanyam, R. Maheshwari, and R. Balasubramanian, Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking, Signal Processing, vol.92, issue.6, pp.1467-1479, 2012.
DOI : 10.1016/j.sigpro.2011.12.005

X. Tan and B. Triggs, Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions, IEEE Trans. Image Process. 2010, vol.19, pp.1635-1650
DOI : 10.1007/978-3-540-75690-3_13
URL : https://hal.archives-ouvertes.fr/inria-00548674

S. Murala, R. Maheshwari, and R. Balasubramanian, Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval, IEEE Transactions on Image Processing, vol.21, issue.5, pp.2874-2886, 2012.
DOI : 10.1109/TIP.2012.2188809

M. Verma and B. Raman, Local tri-directional patterns: A new texture feature descriptor for image retrieval, Digital Signal Processing, vol.51, pp.62-72, 2016.
DOI : 10.1016/j.dsp.2016.02.002

S. Murala, Q. J. Wu, R. Balasubramanian, and R. Maheshwari, Joint histogram between color and local extrema patterns for object tracking, Video Surveillance and Transportation Imaging Applications, 2013.
DOI : 10.1117/12.2002185

I. J. Jacob, K. Srinivasagan, and K. Jayapriya, Local Oppugnant Color Texture Pattern for image retrieval system, Pattern Recognition Letters, vol.42, pp.72-78, 2014.
DOI : 10.1016/j.patrec.2014.01.017

M. Verma, B. Raman, and S. Murala, Local extrema co-occurrence pattern for color and texture image retrieval, Neurocomputing, vol.165, pp.255-269, 2015.
DOI : 10.1016/j.neucom.2015.03.015

G. Qiu, Color image indexing using BTC, IEEE Trans. Image Process, vol.12, pp.93-101, 2003.

M. R. Gahroudi and M. R. Sarshar, Image retrieval based on texture and color method in BTC-VQ compressed domain, Proceedings of the 9th International Symposium on Signal Processing and Its Applications, pp.12-15, 2007.

F. X. Yu, H. Luo, and Z. M. Lu, Colour image retrieval using pattern co-occurrence matrices based on BTC and VQ, Electronics Letters, vol.47, issue.2, pp.100-101, 2011.
DOI : 10.1049/el.2010.3232

J. M. Guo, H. Prasetyo, and H. S. Su, Image indexing using the color and bit pattern feature fusion, Journal of Visual Communication and Image Representation, vol.24, issue.8, pp.1360-1379, 2013.
DOI : 10.1016/j.jvcir.2013.09.005

J. M. Guo and H. Prasetyo, Content-based image retrieval using features extracted from halftoning-based block truncation coding, IEEE Trans. Image Process, vol.24, pp.1010-1024, 2015.

J. M. Guo, H. Prasetyo, and J. H. Chen, Content-based image retrieval using error diffusion block truncation coding features, IEEE Trans. Circuits Syst. Video Technol, vol.25, pp.466-481, 2015.

J. M. Guo, H. Prasetyo, and N. J. Wang, Effective Image Retrieval System Using Dot-Diffused Block Truncation Coding Features, IEEE Transactions on Multimedia, vol.17, issue.9, pp.1576-1590, 2015.
DOI : 10.1109/TMM.2015.2449234

C. Li, G. Duan, and F. Zhong, Rotation Invariant Texture Retrieval Considering the Scale Dependence of Gabor Wavelet, IEEE Trans. Image Process, vol.24, pp.2344-2354, 2015.

M. T. Pham, G. Mercier, and J. Michel, Pointwise Graph-Based Local Texture Characterization for Very High Resolution Multispectral Image Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.5, pp.1962-1973, 2015.
DOI : 10.1109/JSTARS.2014.2386902
URL : https://hal.archives-ouvertes.fr/hal-01257849

M. T. Pham, G. Mercier, and J. Michel, PW-COG: An Effective Texture Descriptor for VHR Satellite Imagery Using a Pointwise Approach on Covariance Matrix of Oriented Gradients, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.6, pp.3345-3359, 2016.
DOI : 10.1109/TGRS.2016.2516042

M. T. Pham, G. Mercier, O. Regniers, and J. Michel, Texture Retrieval from VHR Optical Remote Sensed Images Using the Local Extrema Descriptor with Application to Vineyard Parcel Detection, Remote Sensing, vol.494, issue.5, p.368, 2016.
DOI : 10.1109/TIT.1967.1053964
URL : https://hal.archives-ouvertes.fr/hal-01311993

M. T. Pham, G. Mercier, and J. Michel, Textural features from wavelets on graphs for very high resolution panchromatic Pléiades image classification, pp.131-136

M. T. Pham, G. Mercier, and J. Michel, Change Detection Between SAR Images Using a Pointwise Approach and Graph Theory, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.4, pp.2020-2032, 2016.
DOI : 10.1109/TGRS.2015.2493730

M. T. Pham, G. Mercier, O. Regniers, L. Bombrun, and J. Michel, Texture retrieval from very high resolution remote sensing images using local extrema-based descriptors, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.10-15, 2016.
DOI : 10.1109/IGARSS.2016.7729472

W. Förstner and B. Moonen, A metric for covariance matrices In Geodesy-The Challenge of the 3rd Millennium, pp.299-309, 2003.

K. V. Mardia and P. Jupp, Directional Statistics, 2000.
DOI : 10.1002/9780470316979

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
URL : http://www.cs.ubc.ca/~lowe/papers/ijcv03.ps

H. Bay, T. Tuytelaars, and L. Van-gool, SURF: Speeded up robust features, Proceedings of the 9th European Conference on Computer Vision, pp.7-13, 2006.
DOI : 10.1007/11744023_32

T. Tuytelaars and K. Mikolajczyk, Local Invariant Feature Detectors: A Survey, Foundations and Trends?? in Computer Graphics and Vision, vol.3, issue.3, pp.177-280, 2008.
DOI : 10.1561/0600000017
URL : http://homes.esat.kuleuven.be/~tuytelaa/FT_survey_interestpoints08.pdf

I. L. Dryden, A. Koloydenko, and D. Zhou, Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging, The Annals of Applied Statistics, vol.3, issue.3, pp.1102-1123, 2009.
DOI : 10.1214/09-AOAS249
URL : http://doi.org/10.1214/09-aoas249

A. C. Frery, A. D. Nascimento, and R. J. Cintra, Analytic Expressions for Stochastic Distances Between Relaxed Complex Wishart Distributions, IEEE Transactions on Geoscience and Remote Sensing, vol.52, issue.2, pp.1213-1226, 2014.
DOI : 10.1109/TGRS.2013.2248737
URL : http://arxiv.org/pdf/1304.5417

R. Kwitt and P. Meerwald, Salzburg Texture Image Database Available online: http://www.wavelab.at/ sources/STex, 2017.

S. Abdelmounaime and H. Dong-chen, New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis. ISRN Mach. Vis, pp.10-1155, 2013.
DOI : 10.1155/2013/876386
URL : https://doi.org/10.1155/2013/876386

U. Dataset, Scientific Computing Group Available online, 2012.

A. Krizhevsky, I. Sutskever, and G. Hinton, ImageNet classification with deep convolutional neural networks, Proceedings of the Advances in Neural Information Processing Systems, pp.3-6, 2012.
DOI : 10.1162/neco.2009.10-08-881
URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

C. Cusano, P. Napoletano, and R. Schettini, Evaluating color texture descriptors under large variations of controlled lighting conditions, Journal of the Optical Society of America A, vol.33, issue.1, pp.17-30
DOI : 10.1364/JOSAA.33.000017

P. Napoletano, Hand-Crafted vs Learned Descriptors for Color Texture Classification, Proceedings of the International Workshop on Computational Color Imaging, pp.29-31, 2017.
DOI : 10.1007/978-3-319-10590-1_53

M. Subrahmanyam, Q. J. Wu, R. Maheshwari, and R. Balasubramanian, Modified color motif co-occurrence matrix for image indexing and retrieval, Computers & Electrical Engineering, vol.39, issue.3, pp.762-774, 2013.
DOI : 10.1016/j.compeleceng.2012.11.023

S. Leutenegger, M. Chli, and R. Y. Siegwart, BRISK: Binary Robust invariant scalable keypoints, 2011 International Conference on Computer Vision, pp.3-13, 2011.
DOI : 10.1109/ICCV.2011.6126542
URL : http://margaritachli.com/papers/ICCV2011paper.pdf

M. Calonder, V. Lepetit, C. Strecha, and P. Fua, BRIEF: Binary Robust Independent Elementary Features, In Computer Vision?ECCV, pp.778-792, 2010.
DOI : 10.1007/978-3-642-15561-1_56
URL : http://cvlab.epfl.ch/publications/publications/2010/LepetitF10.pdf

P. Southam and R. Harvey, Texture classification via morphological scale-space: Tex-Mex features, Journal of Electronic Imaging, vol.18, issue.4, p.43007, 2009.
DOI : 10.1117/1.3258441

A. Desai, D. J. Lee, and D. Ventura, Matching affine features with the SYBA feature descriptor This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, International Symposium on Visual Computing, pp.448-457, 2014.