Smooth-Invariant Gaussian Features for Dynamic Texture Recognition

An efficient framework for dynamic texture (DT) representation is proposed by exploiting local features based on Local Binary Patterns (LBP) from filtered images. First, Gaussian smoothing filter is used to deal with near uniform regions and noise which are typical restrictions of LBP operator. Second, the receptive field of Difference of Gaussians (DoG), which is exploited in DT description for the first time, allows to make the descriptor more robust against the changes of environment, illumination, and scale which are main challenges in DT representation. Experimental results of DT recognition on different benchmark datasets (i.e., UCLA, DynTex, and DynTex++), which give outstanding performance compared to the state of the art, verify the interest of our proposal.

[1]  Vipin Tyagi,et al.  Improved Weber’s law based local binary pattern for dynamic texture recognition , 2017, Multimedia Tools and Applications.

[2]  Hyun Seung Yang,et al.  D3: Recognizing dynamic scenes with deep dual descriptor based on key frames and key segments , 2017, Neurocomputing.

[3]  Yong Xu,et al.  Scale-space texture description on SIFT-like textons , 2012, Comput. Vis. Image Underst..

[4]  Yong Xu,et al.  Spatiotemporal lacunarity spectrum for dynamic texture classification , 2017, Comput. Vis. Image Underst..

[5]  Matti Pietikäinen,et al.  Dynamic texture and scene classification by transferring deep image features , 2015, Neurocomputing.

[6]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Narendra Ahuja,et al.  Maximum Margin Distance Learning for Dynamic Texture Recognition , 2010, ECCV.

[8]  Gang Wang,et al.  Optimizing LBP Structure For Visual Recognition Using Binary Quadratic Programming , 2014, IEEE Signal Processing Letters.

[9]  Mark J. Huiskes,et al.  DynTex: A comprehensive database of dynamic textures , 2010, Pattern Recognit. Lett..

[10]  Yong Wang,et al.  Chaotic features for dynamic textures recognition , 2016, Soft Comput..

[11]  Payam Saisan,et al.  Dynamic texture recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Thanh Phuong Nguyen,et al.  Topological Attribute Patterns for texture recognition , 2016, Pattern Recognit. Lett..

[13]  Thanh Phuong Nguyen,et al.  Statistical binary patterns for rotational invariant texture classification , 2016, Neurocomputing.

[14]  Thanh Tuan Nguyen,et al.  Directional Beams of Dense Trajectories for Dynamic Texture Recognition , 2018, ACIVS.

[15]  Yan Huang,et al.  Dynamic Texture Recognition via Orthogonal Tensor Dictionary Learning , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[16]  Thanh Tuan Nguyen,et al.  Completed statistical adaptive patterns on three orthogonal planes for recognition of dynamic textures and scenes , 2018, J. Electronic Imaging.

[17]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[18]  Xudong Jiang,et al.  Dynamic texture recognition using enhanced LBP features , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[19]  Yong Xu,et al.  Classifying dynamic textures via spatiotemporal fractal analysis , 2015, Pattern Recognit..

[20]  Matti Pietikäinen,et al.  Multi-scale Binary Patterns for Texture Analysis , 2003, SCIA.

[21]  Vipin Tyagi,et al.  A novel scheme based on local binary pattern for dynamic texture recognition , 2016, Comput. Vis. Image Underst..

[22]  Shervin Rahimzadeh Arashloo,et al.  Dynamic texture representation using a deep multi-scale convolutional network , 2017, J. Vis. Commun. Image Represent..

[23]  Thanh Tuan Nguyen,et al.  Completed local structure patterns on three orthogonal planes for dynamic texture recognition , 2017, 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA).

[24]  Vipin Tyagi,et al.  Dynamic texture recognition using multiresolution edge-weighted local structure pattern , 2017, Comput. Electr. Eng..

[25]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[26]  Hui Ji,et al.  Equiangular Kernel Dictionary Learning with Applications to Dynamic Texture Analysis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[27]  Yang Zhao,et al.  Completed Local Binary Count for Rotation Invariant Texture Classification , 2012, IEEE Transactions on Image Processing.

[28]  Paul F. Whelan,et al.  Convolutional neural network on three orthogonal planes for dynamic texture classification , 2017, Pattern Recognit..

[29]  Yaping Lin,et al.  Dynamic Texture Recognition Using Volume Local Binary Count Patterns With an Application to 2D Face Spoofing Detection , 2018, IEEE Transactions on Multimedia.

[30]  Loong Fah Cheong,et al.  Synergizing spatial and temporal texture , 2002, IEEE Trans. Image Process..

[31]  Michel Ménard,et al.  Characterization and recognition of dynamic textures based on the 2D+T curvelet transform , 2015, Signal Image Video Process..

[32]  Josef Kittler,et al.  Dynamic Texture Recognition Using Multiscale Binarized Statistical Image Features , 2014, IEEE Transactions on Multimedia.

[33]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Oksam Chae,et al.  Spatiotemporal Directional Number Transitional Graph for Dynamic Texture Recognition , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Vipin Tyagi,et al.  Dynamic texture recognition based on completed volume local binary pattern , 2016, Multidimens. Syst. Signal Process..