Dynamic Texture Recognition Using 3D Random Features

In this paper, we present a novel, simple but effective approach for dynamic texture recognition using 3D random features. Compared with the existing dynamic texture recognition approaches using carefully designed features for high performance, our method use only a few 3D random filters to extract spatio-temporal features from local dynamic texture blocks, which are further encoded into a low-dimensional feature vector. To explore the representative power of the 3D random features, we use two different encoding schemes, the learning-based Fisher vector encoding and the learning-free binary encoding. The proposed method is tested on the UCLA and DynTex databases with various evaluation protocols. Experimental results demonstrate the high performance of our method for dynamic texture recognition.

[1]  Yaping Lin,et al.  Dynamic texture recognition using multiscale PCA-learned filters , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[2]  Zhiyong Yuan,et al.  Local binary pattern based texture analysis for visual fire recognition , 2010, 2010 3rd International Congress on Image and Signal Processing.

[3]  Richard P. Wildes,et al.  A Spatiotemporal Oriented Energy Network for Dynamic Texture Recognition , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

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

[5]  Kibok Lee,et al.  Towards Understanding the Invertibility of Convolutional Neural Networks , 2017, IJCAI.

[6]  Paul W. Fieguth,et al.  Texture Classification from Random Features , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[8]  Guillermo Sapiro,et al.  Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy? , 2015, IEEE Transactions on Signal Processing.

[9]  Paul W. Fieguth,et al.  Fusing Sorted Random Projections for Robust Texture and Material Classification , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  René Vidal,et al.  Categorizing Dynamic Textures Using a Bag of Dynamical Systems , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Hongbin Zha,et al.  Sorted Random Projections for robust texture classification , 2011, 2011 International Conference on Computer Vision.

[12]  Hyun Seung Yang,et al.  Not all frames are equal: aggregating salient features for dynamic texture classification , 2018, Multidimens. Syst. Signal Process..

[13]  Yoshua Bengio,et al.  On random weights for texture generation in one layer CNNS , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Yong Wang,et al.  Exploiting high level feature for dynamic textures recognition , 2015, Neurocomputing.

[15]  Zhenghao Chen,et al.  On Random Weights and Unsupervised Feature Learning , 2011, ICML.

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

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

[18]  Thomas Mensink,et al.  Improving the Fisher Kernel for Large-Scale Image Classification , 2010, ECCV.

[19]  David A. Clausi,et al.  Sorted random projections for robust rotation-invariant texture classification , 2012, Pattern Recognit..

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

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

[22]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

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

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

[25]  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.

[26]  Yong Xu,et al.  Wavelet Domain Multifractal Analysis for Static and Dynamic Texture Classification , 2013, IEEE Transactions on Image Processing.

[27]  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.

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

[29]  Alexandr Andoni,et al.  Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[30]  Yong Xu,et al.  Dynamic texture classification using dynamic fractal analysis , 2011, 2011 International Conference on Computer Vision.

[31]  Yann LeCun,et al.  What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[32]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.