Singular Patterns in Optical Flows as Dynamic Texture Descriptors

  • Leandro N. CoutoEmail author
  • Celia A. Z. Barcelos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


This work introduces a novel approach to dynamic texture description. The proposed method is based on statistics from a vector field feature extractor that decomposes and describes features of distinctive local vector patterns as composites of singular patterns from a dictionary. The extractor is applied to a time-varying vector field, namely a dynamic texture’s optical flow frames. An interest point pooling method statistically highlights the recurring texture patterns, generating a histogram signature that is descriptive of the temporal changes in the texture. The proposed descriptor is used as feature vector on classification experiments in a widespread dataset. The classification results demonstrate our method improves on the state of the art for dynamic textures with non-trivial motion, while employing a smaller feature vector.


Dynamic textures Optical flow Singular patterns 


  1. 1.
    Chao, H., Gu, Y., Napolitano, M.: A survey of optical flow techniques for robotics navigation applications. J. Intell. Rob. Syst. 73(1–4), 361–372 (2014)CrossRefGoogle Scholar
  2. 2.
    Chetverikov, D., Péteri, R.: A brief survey of dynamic texture description and recognition. In: Kurzyński, M., Puchała, E., Żołnierek, A. (eds.) Computer Recognition Systems, vol. 30, pp. 17–26. Springer, Heidelberg (2005). Scholar
  3. 3.
    Couto, L., Backes, A., Barcelos, C.: Texture characterization via deterministic walks’ direction histogram applied to a complex network-based image transformation. Pattern Recogn. Lett. 97, 77–83 (2017)CrossRefGoogle Scholar
  4. 4.
    Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision ECCV. vol. 1, pp. 1–2. Prague (2004)Google Scholar
  5. 5.
    Fazekas, S., Chetverikov, D.: Analysis and performance evaluation of optical flow features for dynamic texture recognition. Sig. Process. Image Commun. 22(7–8), 680–691 (2007)CrossRefGoogle Scholar
  6. 6.
    Gonçalves, W., Machado, B., Bruno, O.: A complex network approach for dynamic texture recognition. Neurocomputing 153, 211–220 (2015)CrossRefGoogle Scholar
  7. 7.
    Hájek, M.: Texture analysis for magnetic resonance imaging. Texture Analysis Magn Resona (2006)Google Scholar
  8. 8.
    Hoey, J., Little, J.: Bayesian clustering of optical flow fields, p. 1086. IEEE (2003)Google Scholar
  9. 9.
    Jiang, M., Machiraju, R., Thompson, D.: Detection and visualization of The Visualization Handbook 295 (2005)Google Scholar
  10. 10.
    Kihl, O., Tremblais, B., Augereau, B.: Multivariate orthogonal polynomials to extract singular points. In: Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on. pp. 857–860. IEEE (2008)Google Scholar
  11. 11.
    Liu, W., Ribeiro, E.: Detecting singular patterns in 2d vector fields using weighted laurent polynomial. Pattern Recogn. 45, 3912–3925 (2012)CrossRefGoogle Scholar
  12. 12.
    Liu, Y., Zhang, J., Yan, W., Zhao, G., Fu, X.: A main directional mean optical flow feature for spontaneous micro-expression recognition. IEEE Trans. Affect. Comput. 7(4), 299–310 (2016)CrossRefGoogle Scholar
  13. 13.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  14. 14.
    Lucas, B., Kanade, T., et al.: An iterative image registration technique with an application to stereo vision. IJCAI 81, 674–679 (1981)Google Scholar
  15. 15.
    Mahbub, U., Imtiaz, H., Roy, T., Rahman, S., Ahad, A.: A template matching approach of one-shot-learning gesture recognition. Pattern Recogn. Lett. 34(15), 1780–1788 (2013)CrossRefGoogle Scholar
  16. 16.
    Rao, R., Jain, R.: Computerized flow field analysis: oriented texture fields. IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 693–709 (1992)CrossRefGoogle Scholar
  17. 17.
    Zhang, J., Marszałek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: a comprehensive study. Int. J. Comput. Vis. 73(2), 213–238 (2007)CrossRefGoogle Scholar
  18. 18.
    Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 915–928 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculdade de ComputaçãoUniversidade Federal de UberlândiaUberlândiaBrazil
  2. 2.Faculdade de MatemáticaUniversidade Federal de UberlândiaUberlândiaBrazil

Personalised recommendations