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. R. Arashloo and J. Kittler, Dynamic texture recognition using multiscale binarized statistical image features, IEEE Trans. Multimedia, vol.16, issue.8, pp.2099-2109, 2014.

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

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, issue.12, pp.2353-2366, 2014.

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.

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.

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

Z. Guo, L. Zhang, and D. Zhang, A completed modeling of local binary pattern operator for texture classification, IEEE Trans. IP, vol.19, issue.6, pp.1657-1663, 2010.

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.

H. Ji, X. Yang, H. Ling, and Y. Xu, Wavelet domain multifractal analysis for static and dynamic texture classification, IEEE Trans. IP, vol.22, issue.1, pp.286-299, 2013.

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

T. P. Nguyen, A. Manzanera, W. G. Kropatsch, and X. S. N'guyen, Topological attribute patterns for texture recognition, Pattern Recog. Letters, vol.80, pp.91-97, 2016.
DOI : 10.1016/j.patrec.2016.06.003
URL : https://hal.archives-ouvertes.fr/hal-01451126

T. P. Nguyen, N. Vu, and A. Manzanera, Statistical binary patterns for rotational invariant texture classification, Neurocomputing, vol.173, pp.1565-1577, 2016.
DOI : 10.1016/j.neucom.2015.09.029
URL : https://hal.archives-ouvertes.fr/hal-01245103

T. T. Nguyen, T. P. Nguyen, and F. Bouchara, Completed local structure patterns on three orthogonal planes for dynamic texture recognition, IPTA. pp, pp.1-6, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01786823

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, issue.05, p.53044, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01978443

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

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

T. Ojala, M. Pietikäinen, and T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. PAMI, vol.24, issue.7, pp.971-987, 2002.
DOI : 10.1109/tpami.2002.1017623

C. Peh and L. F. Cheong, Synergizing spatial and temporal texture, IEEE Trans. IP, vol.11, issue.10, pp.1179-1191, 2002.

R. Péteri, S. Fazekas, and M. J. Huiskes, Dyntex: A comprehensive database of dynamic textures, Pattern Recognition Letters, vol.31, issue.12, pp.1627-1632, 2010.

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.
DOI : 10.1016/j.neucom.2015.07.071
URL : http://arxiv.org/pdf/1502.00303

Y. Quan, C. Bao, and H. Ji, Equiangular kernel dictionary learning with applications to dynamic texture analysis, CVPR. pp, pp.308-316, 2016.
DOI : 10.1109/cvpr.2016.40

Y. Quan, Y. Huang, and H. Ji, Dynamic texture recognition via orthogonal tensor dictionary learning, pp.73-81, 2015.
DOI : 10.1109/iccv.2015.17

Y. Quan, Y. Sun, and Y. Xu, Spatiotemporal lacunarity spectrum for dynamic texture classification, CVIU, vol.165, pp.85-96, 2017.
DOI : 10.1016/j.cviu.2017.10.008

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

J. Ren, X. Jiang, and J. Yuan, Dynamic texture recognition using enhanced LBP features, ICASSP. pp, pp.2400-2404, 2013.
DOI : 10.1109/icassp.2013.6638085

J. Ren, X. Jiang, J. Yuan, and G. Wang, Optimizing LBP structure for visual recognition using binary quadratic programming, SPL, vol.21, issue.11, pp.1346-1350, 2014.

A. R. Rivera and O. Chae, Spatiotemporal directional number transitional graph for dynamic texture recognition, IEEE Trans. PAMI, vol.37, issue.10, pp.2146-2152, 2015.
DOI : 10.1109/tpami.2015.2392774
URL : https://doi.org/10.1109/tpami.2015.2392774

P. Saisan, G. Doretto, Y. N. Wu, and S. Soatto, Dynamic texture recognition, CVPR. pp, pp.58-63, 2001.
DOI : 10.1109/cvpr.2001.990925

D. Tiwari and V. Tyagi, Dynamic texture recognition based on completed volume local binary pattern, MSSP, vol.27, issue.2, pp.563-575, 2016.
DOI : 10.1007/s11045-015-0319-6

D. Tiwari and V. Tyagi, A novel scheme based on local binary pattern for dynamic texture recognition, CVIU, vol.150, pp.58-65, 2016.
DOI : 10.1016/j.cviu.2016.04.010

D. Tiwari and V. Tyagi, Improved weber's law based local binary pattern for dynamic texture recognition, Multimedia Tools Appl, vol.76, issue.5, pp.6623-6640, 2017.
DOI : 10.1007/s11042-016-3362-x

D. Tiwari and V. Tyagi, Dynamic texture recognition using multiresolution edgeweighted local structure pattern, Computers & Electrical Engineering, vol.62, pp.485-498, 2017.
DOI : 10.1016/j.compeleceng.2016.11.008

N. Vu, T. P. Nguyen, and C. Garcia, Improving texture categorization with biologically-inspired filtering, Image Vision Comput, vol.32, issue.6-7, pp.424-436, 2014.
DOI : 10.1016/j.imavis.2014.04.006
URL : https://hal.archives-ouvertes.fr/hal-01584903

Y. Wang and S. Hu, Chaotic features for dynamic textures recognition, Soft Computing, vol.20, issue.5, pp.1977-1989, 2016.
DOI : 10.1007/s00500-015-1618-4

Y. Xu, S. B. Huang, H. Ji, and C. Fermüller, Scale-space texture description on siftlike textons, CVIU, vol.116, issue.9, pp.999-1013, 2012.
DOI : 10.1016/j.cviu.2012.05.003

Y. Xu, Y. Quan, Z. Zhang, H. Ling, and H. Ji, Classifying dynamic textures via spatiotemporal fractal analysis, Pattern Recognition, vol.48, issue.10, pp.3239-3248, 2015.
DOI : 10.1016/j.patcog.2015.04.015

G. Zhao and M. Pietikäinen, Dynamic texture recognition using local binary patterns with an application to facial expressions, IEEE Trans. PAMI, vol.29, issue.6, pp.915-928, 2007.
DOI : 10.1109/tpami.2007.1110
URL : http://www.ee.oulu.fi/mvg/files/pdf/pdf_740.pdf

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, issue.3, pp.552-566, 2018.
DOI : 10.1109/tmm.2017.2750415

Y. Zhao, D. S. Huang, and W. Jia, Completed Local Binary Count for Rotation Invariant Texture Classification, IEEE Trans. IP, vol.21, issue.10, pp.4492-4497, 2012.