Skip to main content
SpringerLink
Log in

Dynamic Texture Classification Based on 3D ICA-Learned Filters and Fisher Vector Encoding in Big Data Environment

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

Many researchers focus on the local feature-based description of the dynamic texture, because of its stability and low dimensionality. Among the existing dynamic texture description methods, many are learning-free and may not adapt well to new data. Some others try to apply complex learning techniques such as deep learning and provide only average performance due to the lack ness of a large-scale dataset. Thus, in this paper we propose a dynamic texture feature extraction method based on 3D filter learning and fisher vector coding, trying to achieve good performance by applying learning techniques in the big data environment. The proposed method includes two learning stages 1) independent component analysis is adopted for 3D filter learning and 2) parameters of the Gaussian mixture model are learned via training on the randomly sampled 3D local blocks. The learned filters are used to extract local features i.e., the filter responses, of which the first-order and second-order characteristics are captured by fisher vector encoding. This work has three advantages: 1) our method learns features from training data and can be well generalized to new data, 2) spatial and temporal features are captured simultaneously 3) as a local processing method, our method has no demand for a large number of computing resources to solve the big data classification problem. The method’s viability has been demonstrated on three benchmark dynamic texture datasets, UCLA, DynTex, and DynTex++. Under various evaluation protocols, the recognition rates of the proposed method are better than that of some advanced methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11

Similar content being viewed by others

References

  1. Doretto, G., Chiuso, A., Wu, Y. N., & Soatto, S. (2003). Dynamic textures. International Journal of Computer Vision, 51(2), 91–109.

    Article  MATH  Google Scholar 

  2. Saisan, P., Doretto, G., Wu, Y. N., & Soatto, S. (2001). Dynamic texture recognition. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 2, pp. II–II. IEEE

  3. Gai, K., Qiu, M., Thuraisingham, B., & Tao, L. (2015). Proactive attribute-based secure data schema for mobile cloud in financial industry. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, pp. 1332–1337. IEEE

  4. Chetverikov, D., & Péteri, R. (2005). A brief survey of dynamic texture description and recognition. In: Computer Recognition Systems, pp. 17–26. Springer 

  5. Zhao, G., & Pietikainen, M. (2007). Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE transactions on pattern analysis and machine intelligence, 29(6), 915–928.

    Article  Google Scholar 

  6. Rivera, A. R., & Chae, O. (2015). Spatiotemporal directional number transitional graph for dynamic texture recognition. IEEE transactions on pattern analysis and machine intelligence, 37(10), 2146–2152.

    Article  Google Scholar 

  7. Yasmin, S., Pathan, R. K., Biswas, M., Khandaker, M. U., & Faruque, M. R. I. (2020). Development of a robust multi-scale featured local binary pattern for improved facial expression recognition. Sensors, 20(18), 5391.

    Article  Google Scholar 

  8. Barmpoutis, P., Stathaki, T., Dimitropoulos, K., & Grammalidis, N. (2020). Early fire detection based on aerial 360-degree sensors, deep convolution neural networks and exploitation of fire dynamic textures. Remote Sensing, 12(19), 3177.

    Article  Google Scholar 

  9. Zhdanova, M., Voronin, V., Semenishchev, E., Ilyukhin, Y., & Zelensky, A. (2020). Human activity recognition for efficient human-robot collaboration. In: Artificial Intelligence and Machine Learning in Defense Applications II, vol. 11543, p. 115430K. International Society for Optics and Photonics

  10. Xu, Y., Quan, Y., Ling, H., & Ji, H. (2011). Dynamic texture classification using dynamic fractal analysis. In: 2011 international conference on computer vision, pp. 1219–1226. IEEE.

  11. Zhao, X., Lin, Y., & Heikkilä, J. (2017). Dynamic texture recognition using volume local binary count patterns with an application to 2d face spoofing detection. IEEE Transactions on Multimedia, 20(3), 552–566.

    Article  Google Scholar 

  12. Quan, Y., Huang, Y., & Ji, H. (2015). Dynamic texture recognition via orthogonal tensor dictionary learning. In: Proceedings of the IEEE international conference on computer vision, pp. 73–81.

  13. Zhao, X., Lin, Y., Liu, L., Heikkilä, J., & Zheng, W. (2019). Dynamic texture classification using unsupervised 3d filter learning and local binary encoding. IEEE Transactions on Multimedia, 21(7), 1694–1708.

    Article  Google Scholar 

  14. Péteri, R., Fazekas, S., & Huiskes, M. J. (2010). Dyntex: A comprehensive database of dynamic textures. Pattern Recognition Letters, 31(12), 1627–1632.

    Article  Google Scholar 

  15. Chan, T. H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015). Pcanet: A simple deep learning baseline for image classification? IEEE transactions on image processing, 24(12), 5017–5032.

    Article  MathSciNet  MATH  Google Scholar 

  16. Kannala, J., & Rahtu, E. (2012). Bsif: Binarized statistical image features. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012), pp. 1363–1366. IEEE.

  17. Arashloo, S. R., & Kittler, J. (2014). Dynamic texture recognition using multiscale binarized statistical image features. IEEE Transactions on Multimedia, 16(8), 2099–2109.

    Article  Google Scholar 

  18. Zhao, X., Lin, Y., & Heikkilä, J. (2017). Dynamic texture recognition using multiscale pca-learned filters. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 4152–4156. IEEE.

  19. Arashloo, S. R., Amirani, M. C., & Noroozi, A. (2017). Dynamic texture representation using a deep multi-scale convolutional network. Journal of Visual Communication and Image Representation, 43, 89–97.

    Article  Google Scholar 

  20. Hong, S., Ryu, J., & Yang, H. S. (2018). Not all frames are equal: Aggregating salient features for dynamic texture classification. Multidimensional Systems and Signal Processing, 29(1), 279–298.

    Article  MathSciNet  Google Scholar 

  21. Andrearczyk, V., & Whelan, P. F. (2018). Convolutional neural network on three orthogonal planes for dynamic texture classification. Pattern Recognition, 76, 36–49.

    Article  Google Scholar 

  22. Chen, S., Li, W., Yang, H., Huang, D., & Wang, Y. (2020). 3d face mask anti-spoofing via deep fusion of dynamic texture and shape clues. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pp. 314–321. IEEE.

  23. Paier, W., Hilsmann, A., & Eisert, P. (2020). Interactive facial animation with deep neural networks. IET Computer Vision, 14(6), 359–369.

    Article  Google Scholar 

  24. Qiu, M., Ming, Z., Li, J., Liu, J., Quan, G., & Zhu, Y. (2013). Informer homed routing fault tolerance mechanism for wireless sensor networks. Journal of Systems Architecture, 59(4–5), 260–270.

    Article  Google Scholar 

  25. Qiu, M., Zhang, L., Zhong, Ming, Zhi, C., Qin, X., & Yang, L. T. (2013). Security-aware optimization for ubiquitous computing systems with SEAT graph approach. Journal of Computer and System Sciences, 79(5), 518–529. https://doi.org/10.1016/j.jcss.2012.11.002.

    Article  MathSciNet  MATH  Google Scholar 

  26. Qiu, M., Chen, Z., Niu, J., Zong, Z., Quan, G., Qin, X., & Yang, L. T. (2015). Data Allocation for Hybrid Memory With Genetic Algorithm. IEEE Transactions on Emerging Topics in Computing, 3(4), 544–555. https://doi.org/10.1109/TETC.2015.2398824.

    Article  Google Scholar 

  27. Liu, L., Fieguth, P., Guo, Y., Wang, X., & Pietikäinen, M. (2017). Local binary features for texture classification: Taxonomy and experimental study. Pattern Recognition, 62, 135–160.

    Article  Google Scholar 

  28. Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015). Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp. 4489–4497.

  29. Perronnin, F., Sánchez, J., & Mensink, T. (2010). Improving the fisher kernel for large-scale image classification. In: European conference on computer vision, pp. 143–156. Springer.

  30. Zhao, X., Lin, Y.,  &Liu, L. (2019). Dynamic texture recognition using 3d random features. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2102–2106. IEEE.

  31. Tang, X., Li, K., Qiu, M., & Sha, E. H. M. (2012). A hierarchical reliability-driven scheduling algorithm in grid systems. Journal of Parallel and Distributed Computing, 72(4), 525–535.

    Article  Google Scholar 

  32. Su, H., Qiu, M., & Wang, H. (2012). Secure wireless communication system for smart grid with rechargeable electric vehicles. IEEE Communications Magazine, 50(8), 62–68.

    Article  Google Scholar 

  33. Li, J., Qiu, M., Niu, J., Gao, W., Zong, Z., & Qin, X. (2010). Feedback dynamic algorithms for preemptable job scheduling in cloud systems. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 561–564. IEEE.

  34. Mumtaz, A., Coviello, E., Lanckriet, G. R., & Chan, A. B. (2012). Clustering dynamic textures with the hierarchical em algorithm for modeling video. IEEE transactions on pattern analysis and machine intelligence, 35(7), 1606–1621.

    Article  Google Scholar 

  35. Ji, H., Yang, X., Ling, H., & Xu, Y. (2012). Wavelet domain multifractal analysis for static and dynamic texture classification. IEEE Transactions on Image Processing, 22(1), 286–299.

    Article  MathSciNet  MATH  Google Scholar 

  36. Xu, Y., Huang, S., Ji, H., & Fermüller, C. (2012). Scale-space texture description on sift-like textons. Computer Vision and Image Understanding, 116(9), 999–1013.

    Article  Google Scholar 

  37. Harandi, M., Sanderson, C., Shen, C., & Lovell, B. C. (2013). Dictionary learning and sparse coding on grassmann manifolds: An extrinsic solution. In: Proceedings of the IEEE international conference on computer vision, pp. 3120–3127.

  38. Dubois, S., Péteri, R., & Ménard, M. (2015). Characterization and recognition of dynamic textures based on the 2d+ t curvelet transform. Signal, Image and Video Processing, 9(4), 819–830.

    Article  Google Scholar 

  39. Andrearczyk, V., & Whelan, P. F. (2015). Dynamic texture classification using combined co-occurrence matrices of optical flow. In: Irish Machine Vision & Image Processing Conference proceedings IMVIP, vol. 2015

  40. Derpanis, K. G., & Wildes, R. (2011). Spacetime texture representation and recognition based on a spatiotemporal orientation analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(6), 1193–1205.

    Article  Google Scholar 

  41. Ghanem, B., & Ahuja, N. (2010). Maximum margin distance learning for dynamic texture recognition. In: European Conference on Computer Vision, pp. 223–236. Springer.

  42. Ren, J., Jiang, X., & Yuan, J. (2013). Dynamic texture recognition using enhanced lbp features. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2400–2404. IEEE.

  43. Tiwari, D., & Tyagi, V. (2016). A novel scheme based on local binary pattern for dynamic texture recognition. Computer Vision and Image Understanding, 150, 58–65.

    Article  Google Scholar 

  44. Tiwari, D., & Tyagi, V. (2016). Dynamic texture recognition based on completed volume local binary pattern. Multidimensional Systems and Signal Processing, 27(2), 563–575.

    Article  Google Scholar 

  45. Derpanis, K.G., & Wildes, R.P. (2010). Dynamic texture recognition based on distributions of spacetime oriented structure. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 191–198. IEEE.

  46. Feichtenhofer, C., Pinz, A., & Wildes, R.P. (2014). Bags of spacetime energies for dynamic scene recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2681–2688.

  47. Hadji, I., & Wildes, R. P. (2017). A spatiotemporal oriented energy network for dynamic texture recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3066–3074.

  48. Shao, J., Loy, C. C., Kang, K., & Wang, X. (2016). Slicing convolutional neural network for crowd video understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5620–5628.

  49. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014). Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1725–1732.

  50. Quan, Y., Bao, C., & Ji, H. (2016). Equiangular kernel dictionary learning with applications to dynamic texture analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 308–316.

  51. Ravichandran, A., Chaudhry, R., & Vidal, R. (2009). View-invariant dynamic texture recognition using a bag of dynamical systems. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 1651–1657. IEEE.

Download references

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No.61972136), and Hubei Natural Science Foundation (No.2020CFB497, No.2020CFB571), MOE(Ministry of Education in China) Project of Humanities and Social Sciences (No.20YJAZH112), the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation (No.T201410, T2020017), the Science and Technology Research Projects of Hubei Provincial Department of Education (No.Q20162706).

Author information

Authors and Affiliations

  1. School of Computer and Information Science, Hubei Engineering Univerisity, Xiaogan, 432000, China

    Zenggang Xiong, Fangrong Mo, Xiaochao Zhao, Fang Xu, Xuemin Zhang & Yunyun Wu

  2. School of Computer Science and Information Engineering, Hubei University, Wuhan Hubei, 430062, China

    Zenggang Xiong, Fangrong Mo, Xiaochao Zhao, Fang Xu, Xuemin Zhang & Yunyun Wu

Authors
  1. Zenggang Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fangrong Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaochao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuemin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yunyun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaochao Zhao.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on: Big Data Security Track

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiong, Z., Mo, F., Zhao, X. et al. Dynamic Texture Classification Based on 3D ICA-Learned Filters and Fisher Vector Encoding in Big Data Environment. J Sign Process Syst 94, 1129–1143 (2022). https://doi.org/10.1007/s11265-021-01737-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-021-01737-0

Keywords

Search

Navigation