P. Saisan, G. Doretto, Y. N. Wu, and S. Soatto, Dynamic texture recognition, CVPR, pp.58-63, 2001.

A. B. Chan and N. Vasconcelos, Modeling, clustering, and segmenting video with mixtures of dynamic textures, IEEE Trans. PAMI, vol.30, pp.909-926, 2008.

X. S. Nguyen, T. P. Nguyen, F. Charpillet, and N. S. Vu, Local derivative pattern for action recognition in depth images, Multimedia Tools Appl, vol.77, pp.8531-8549, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01657473

G. Zhao and M. Pietikäinen, Dynamic texture recognition using local binary patterns with an application to facial expressions, IEEE Trans. PAMI, vol.29, pp.915-928, 2007.

W. Zhang, M. L. Smith, L. N. Smith, and A. R. Farooq, Gender and gaze gesture recognition for human-computer interaction, CVIU, vol.149, pp.32-50, 2016.

A. I. Maqueda, C. R. Blanco, F. Jaureguizar, and N. N. García, Humancomputer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns, CVIU, vol.141, pp.126-137, 2015.

P. Barmpoutis, K. Dimitropoulos, and N. Grammalidis, Smoke detection using spatio-temporal analysis, motion modeling and dynamic texture recognition, pp.1078-1082, 2014.

P. Mettes, R. T. Tan, and R. C. Veltkamp, Water detection through spatiotemporal invariant descriptors, CVIU, vol.154, pp.182-191, 2017.

T. P. Nguyen, A. Manzanera, M. Garrigues, and N. Vu, Spatial motion patterns: Action models from semi-dense trajectories, IJPRAI, vol.28, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01118257

C. Peh and L. F. Cheong, Synergizing spatial and temporal texture, IEEE Trans. IP, vol.11, pp.1179-1191, 2002.

R. Péteri, D. Chetverikov, K. W. Wojciechowski, B. Smolka, H. Palus et al., Qualitative characterization of dynamic textures for video retrieval, Computational Imaging and Vision, vol.32, pp.33-38, 2004.

R. Péteri and D. Chetverikov, Dynamic texture recognition using normal flow and texture regularity, LNCS, vol.3523, pp.223-230, 2005.

Z. Lu, W. Xie, J. Pei, and J. Huang, Dynamic texture recognition by spatio-temporal multiresolution histograms, pp.241-246, 2005.

T. T. Nguyen, T. P. Nguyen, F. Bouchara, and X. S. Nguyen, Directional beams of dense trajectories for dynamic texture recognition, ACIVS, pp.74-86, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01860067

A. R. Rivera and O. Chae, Spatiotemporal directional number transitional graph for dynamic texture recognition, IEEE Trans. PAMI, vol.37, pp.2146-2152, 2015.

B. Chan, A. B. Vasconcelos, and N. , Classifying video with kernel dynamic textures, CVPR, pp.1-6, 2007.

A. Mumtaz, E. Coviello, G. R. Lanckriet, and A. B. Chan, Clustering dynamic textures with the hierarchical EM algorithm for modeling video, IEEE Trans. PAMI, vol.35, pp.1606-1621, 2013.

Y. Wang and S. Hu, Chaotic features for dynamic textures recognition, Soft Computing, vol.20, pp.1977-1989, 2016.

A. Ravichandran, R. Chaudhry, and R. Vidal, View-invariant dynamic texture recognition using a bag of dynamical systems, pp.1651-1657, 2009.

A. Mumtaz, E. Coviello, G. R. Lanckriet, and A. B. Chan, A scalable and accurate descriptor for dynamic textures using bag of system trees, IEEE Trans. PAMI, vol.37, pp.697-712, 2015.

X. Qi, C. G. Li, G. Zhao, X. Hong, and M. Pietikainen, Dynamic texture and scene classification by transferring deep image features, Neurocomputing, vol.171, pp.1230-1241, 2016.

V. Andrearczyk and P. F. Whelan, Convolutional neural network on three orthogonal planes for dynamic texture classification, Pattern Recognition, vol.76, pp.36-49, 2018.

S. R. Arashloo, M. C. Amirani, and A. Noroozi, Dynamic texture representation using a deep multi-scale convolutional network, JVCIR, vol.43, pp.89-97, 2017.

S. Hong, J. Ryu, W. Im, and H. S. Yang, D3: recognizing dynamic scenes with deep dual descriptor based on key frames and key segments, Neurocomputing, vol.273, pp.611-621, 2018.

Y. Quan, Y. Huang, and H. Ji, Dynamic texture recognition via orthogonal tensor dictionary learning, ICCV, pp.73-81, 2015.

Y. Quan, C. Bao, and H. Ji, Equiangular kernel dictionary learning with applications to dynamic texture analysis, CVPR, pp.308-316, 2016.

S. R. Arashloo and J. Kittler, Dynamic texture recognition using multiscale binarized statistical image features, IEEE Trans. Multimedia, vol.16, pp.2099-2109, 2014.

Y. Xu, Y. Quan, H. Ling, and H. Ji, Dynamic texture classification using dynamic fractal analysis, ICCV, pp.1219-1226, 2011.

Y. Xu, S. B. Huang, H. Ji, and C. Fermüller, Scale-space texture description on sift-like textons, CVIU, vol.116, pp.999-1013, 2012.

H. Ji, X. Yang, H. Ling, and Y. Xu, Wavelet domain multifractal analysis for static and dynamic texture classification, IEEE Trans. IP, vol.22, pp.286-299, 2013.

Y. Quan, Y. Sun, and Y. Xu, Spatiotemporal lacunarity spectrum for dynamic texture classification, CVIU, vol.165, pp.85-96, 2017.

M. Baktashmotlagh, M. T. Harandi, A. , C. Lovell, B. C. Salzmann et al., Discriminative non-linear stationary subspace analysis for video classification, IEEE Trans. PAMI, vol.36, pp.2353-2366, 2014.

G. Zhao, T. Ahonen, J. Matas, and M. Pietikäinen, Rotation-invariant image and video description with local binary pattern features, IEEE Trans. IP, vol.21, pp.1465-1477, 2012.

D. Tiwari and V. Tyagi, Dynamic texture recognition based on completed volume local binary pattern, MSSP, vol.27, pp.563-575, 2016.

D. Tiwari and V. Tyagi, A novel scheme based on local binary pattern for dynamic texture recognition, CVIU, vol.150, pp.58-65, 2016.

T. T. Nguyen, T. P. Nguyen, and F. Bouchara, Completed local structure patterns on three orthogonal planes for dynamic texture recognition, pp.1-6, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01873110

T. T. Nguyen, T. P. Nguyen, and F. Bouchara, Completed statistical adaptive patterns on three orthogonal planes for recognition of dynamic textures and scenes, J. Electronic Imaging, vol.27, p.53044, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01978443

J. Ren, X. Jiang, J. Yuan, and G. Wang, Optimizing LBP structure for visual recognition using binary quadratic programming, IEEE Signal Processing Letters, vol.21, pp.1346-1350, 2014.

J. Ren, X. Jiang, and J. Yuan, Dynamic texture recognition using enhanced LBP features, ICASSP, pp.2400-2404, 2013.

T. Ojala, M. Pietikäinen, and T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. PAMI, vol.24, pp.971-987, 2002.

Y. Zhao, D. S. Huang, and W. Jia, Completed Local Binary Count for Rotation Invariant Texture Classification, IEEE Trans. IP, vol.21, pp.4492-4497, 2012.

A. Fathi and A. R. Naghsh-nilchi, Noise Tolerant Local Binary Pattern Operator for Efficient Texture Analysis, Pattern Recognition Letters, vol.33, pp.1093-1100, 2012.

T. P. Nguyen, A. Manzanera, W. G. Kropatsch, and X. S. N'guyen, Topological attribute patterns for texture recognition, Pattern Recognition Letters, vol.80, pp.91-97, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01451126

Z. Guo, L. Zhang, and D. Zhang, A completed modeling of local binary pattern operator for texture classification, IEEE Trans. IP, vol.19, pp.1657-1663, 2010.

Z. Guo, L. Zhang, and D. Zhang, Rotation Invariant Texture Classification using LBP Variance (LBPV) with Global Matching, Pattern Recognition, vol.43, issue.3, pp.706-719, 2010.

T. P. Nguyen, N. Vu, and A. Manzanera, Statistical binary patterns for rotational invariant texture classification, Neurocomputing, vol.173, pp.1565-1577, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01245103

T. Mäenpää and M. Pietikäinen, Multi-scale binary patterns for texture analysis, SCIA, pp.885-892, 2003.

S. Liao, M. W. Law, and A. C. Chung, Dominant local binary patterns for texture classification, IEEE Trans. IP, vol.18, pp.1107-1118, 2009.

X. Tan and B. Triggs, Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions, IEEE Trans. IP, vol.19, pp.1635-1650, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00565029

Y. Guo, G. Zhao, and M. Pietikäinen, Discriminative features for texture description, Pattern Recognition, vol.45, pp.3834-3843, 2012.

L. Nanni, S. Brahnam, and A. Lumini, A Simple Method for Improving Local Binary Patterns by considering Non-uniform Patterns, Pattern Recognition, vol.45, pp.3844-3852, 2012.

N. S. Vu, T. P. Nguyen, and C. Garcia, Improving texture categorization with biologically-inspired filtering, Image and Vision Computing, vol.32, pp.424-436, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01584903

N. Shrivastava and V. Tyagi, An effective scheme for image texture classification based on binary local structure pattern. The Visual Computer, vol.30, pp.1223-1232, 2014.

L. Liu, S. Lao, P. W. Fieguth, Y. Guo, X. Wang et al., Median robust extended local binary pattern for texture classification, IEEE Trans. IP, vol.25, pp.1368-1381, 2016.

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 Trans. IP, vol.19, pp.533-544, 2010.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, pp.2278-2324, 1998.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, pp.1106-1114, 2012.

R. Péteri, S. Fazekas, and M. J. Huiskes, Dyntex: A comprehensive database of dynamic textures, Pattern Recognition Letters, vol.31, pp.1627-1632, 2010.

B. Ghanem and N. Ahuja, Maximum margin distance learning for dynamic texture recognition, LNCS, vol.6312, pp.223-236, 2010.

R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin, LIBLINEAR: A library for large linear classification, JMLR, vol.9, pp.1871-1874, 2008.

D. Tiwari and V. Tyagi, Improved weber's law based local binary pattern for dynamic texture recognition, Multimedia Tools Appl, vol.76, pp.6623-6640, 2017.

D. Tiwari and V. Tyagi, Dynamic texture recognition using multiresolution edge-weighted local structure pattern, Computers & Electrical Engineering, vol.62, pp.485-498, 2017.

X. Zhao, Y. Lin, and J. Heikkilä, Dynamic texture recognition using volume local binary count patterns with an application to 2d face spoofing detection, IEEE Trans. Multimedia, vol.20, pp.552-566, 2018.

K. G. Derpanis and R. P. Wildes, Spacetime texture representation and recognition based on a spatiotemporal orientation analysis, IEEE Trans. PAMI, vol.34, pp.1193-1205, 2012.

S. Dubois, R. Péteri, and M. Ménard, Characterization and recognition of dynamic textures based on the 2d+t curvelet transform. Signal, Image and Video Processing, vol.9, pp.819-830, 2015.

Y. Xu, Y. Quan, Z. Zhang, H. Ling, and H. Ji, Classifying dynamic textures via spatiotemporal fractal analysis, Pattern Recognition, vol.48, pp.3239-3248, 2015.

T. T. Nguyen, T. P. Nguyen, and F. Bouchara, Smooth-invariant gaussian features for dynamic texture recognition, ICIP, pp.4400-4404, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02133547

T. T. Nguyen, T. P. Nguyen, F. Bouchara, and N. Vu, Volumes of blurredinvariant gaussians for dynamic texture classification, pp.155-167, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02160704