Multimedia Tools and Applications

, Volume 76, Issue 5, pp 6623–6640 | Cite as

Improved Weber’s law based local binary pattern for dynamic texture recognition

Article

Abstract

Dynamic texture is the moving sequence of images that shows some form of temporal regularity. Various static texture descriptors have been extended to spatiotemporal domain for dynamic texture classification. Local Binary Pattern (LBP) is a simple descriptor computationally but sensitive to noise and sometimes fails to capture different patterns. In view of this, a novel approach for dynamic texture classification is introduced that maintains the advantageous characteristics of uniform LBP. Inspired by the Weber’s law, a simple yet very powerful, robust texture descriptor, i.e., Weber’s law based LBP with center pixel (WLBPC) is proposed from the local patches based on the conventional Local Binary Pattern approach. A noise resistant variant of Weber’s law based LBP with center pixel (NR-WLBPC) is also proposed. To do this, WLBPC is extended to a 3-valued code based on a new threshold. Proposed noise resistant variant of WLBPC descriptor makes use of the indecisive bit and the uniform pattern to compute the feature vector. Center pixel information is fused with both the dynamic texture descriptors to improve the discriminative power. Extensive experimental evaluations on representative dynamic texture databases (DynTex++ and UCLA) show that the proposed descriptors show better performance in comparison to recent state-of-the-art LBP variants and other methods under both normal and noisy conditions. Noise invariant of the proposed descriptor also performs better in the presence of noise due to its robustness and discriminating capabilities.

Keywords

Dynamic texture Local binary pattern Uniform pattern Noise resistance Weber law 

References

  1. 1.
    Baktashmotlagh M, Harandi M, Lovell BC, Salzmann M (2014) Discriminative non-linear stationary subspace analysis for video classification. IEEE Trans Pattern Anal Mach Intell 36(12):2353–2366CrossRefGoogle Scholar
  2. 2.
    Chan AB, Coviello E, Lanckriet G (2010) Clustering dynamic textures with the hierarchical EM algorithm. IEEE Conf Comput Vis Pattern Recognit 2022–2029Google Scholar
  3. 3.
    Chan C, Goswami B, Kittler J, Christmas WJ (2012) Local ordinal contrast pattern histograms for spatiotemporal, lip-based speaker authentication. IEEE Trans Inf Forensic Secur 7(2):602–612CrossRefGoogle Scholar
  4. 4.
    Chan AB, and Vasconcelos N (2007) Classifying video with kernel dynamic textures. IEEE Conf Comput Vis. Pattern Recognit 1–6Google Scholar
  5. 5.
    Chang AB, Vasconcelos N (2005) Probabilistic kernels for the classification of auto-regressive visual processes. Proc IEEE Conf Comput Vis Pattern Recognit 1:846–851Google Scholar
  6. 6.
    Chen J, Kellokumpu V, Zhao G, Pietikäinen M (2013) RLBP: robust local binary pattern. Proc Br Mach Vis Conf (BMVC 2013)Google Scholar
  7. 7.
    Chen J, Shan S, He C, Zhao G, Pietikäinen M, Chen X, Gao W (2010) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720CrossRefGoogle Scholar
  8. 8.
    Chetverikov D, Péteri R (2005) A brief survey of dynamic texture description and recognition. 4th Int Conf Comput Recognit Syst 17–26Google Scholar
  9. 9.
    Derpanis KG, Wildes RP (2010) Dynamic texture recognition based on distributions of space time oriented structure. IEEE Conf Comput Vis Pattern Recognit (CVPR) 191–198Google Scholar
  10. 10.
    Derpanis KG, Wildes RP (2012) Space time texture representation and recognition based on a spatiotemporal orientation analysis. IEEE Trans Pattern Anal Mach Intell 34(6):1193–1205CrossRefGoogle Scholar
  11. 11.
    Doretto G, Chiuso A, Soatto S, Wu YN (2003) Dynamic textures. Int J Comput Vis 51(2):91–109CrossRefMATHGoogle Scholar
  12. 12.
    Ghanem B, Ahuja N (2010) Maximum margin distance learning for dynamic texture recognition. Eur Conf Comput Vis 6312:223–236Google Scholar
  13. 13.
    Gonçalves WN, Machado BB, Bruno OM Spatiotemporal Gabor filters: a new method for dynamic texture recognition CoRR, http://arxiv.org/abs/1201.3612
  14. 14.
    Jain AK (1989) Fundamentals of digital signal processing. Prentice-Hall, Englewood Cliffs, NJMATHGoogle Scholar
  15. 15.
    Liu L, Long Y, Fieguth PW, Lao S, Zhao G (2013) BRINT: binary rotation invariant and noise tolerant texture classification. IEEE Int Conf Image Proc, ICIP 2013:255–259Google Scholar
  16. 16.
    Liu F, Tang Z, Tang J (2013) WLBP: weber local binary pattern for local image description. Neurocomputing 120:325–335CrossRefGoogle Scholar
  17. 17.
    Liu L, Zhao L, Long Y, Kuang G, Fieguth PW (2012) Extended local binary patterns for texture classification. Image Vis Comput 30(2):86–99CrossRefGoogle Scholar
  18. 18.
    Lowe D (2004) Distinctive image features from scale invariant key points. Int J Comput Vis 60(2):91–110CrossRefGoogle Scholar
  19. 19.
    Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59CrossRefGoogle Scholar
  20. 20.
    Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefMATHGoogle Scholar
  21. 21.
    Péteri R, Fazekas S, Huiskes MJ (2010) DynTex: a comprehensive database of dynamic textures. Pattern Recogn Lett 31(12):1627–1632CrossRefGoogle Scholar
  22. 22.
    Ravichandran A, Chaudhry R, Vidal R (2009) View-invariant dynamic texture recognition using a bag of dynamical systems. IEEE Conf Comput Vis Pattern Recognit (CVPR) 1651–1657Google Scholar
  23. 23.
    Ren J, Jiang XD, and Yuan J (2013) Dynamic texture recognition using enhanced LBP features. IEEE Int Conf Acoust Speech Signal Proc (ICASSP) 2400–2404Google Scholar
  24. 24.
    Ren J, Jiang X, Yuan J (2013) Noise-resistant local binary pattern with an embedded error-correction mechanism. IEEE Trans Image Process 22(10):4049–4060MathSciNetCrossRefGoogle Scholar
  25. 25.
    Rivera AR, Chae O (2015) Spatiotemporal directional number transitional graph for dynamic texture recognition. IEEE Trans Pattern Anal Mach Intell. doi:10.1109/TPAMI.2015.2392774 Google Scholar
  26. 26.
    Saisan P, Doretto G, Wu Y, Soatto S (2001) Dynamic texture recognition. IEEE Conf Comput Vis Pattern Recognit 2:58–63Google Scholar
  27. 27.
    Song T, Li H, Meng F, Wu Q, Luo B, Zeng B, Gabbouj M (2014) Noise-robust texture description using local contrast patterns via global measures. IEEE Signal Proc Lett 21(1):93–96CrossRefGoogle Scholar
  28. 28.
    Srivastava N, Tyagi V (2013) An effective scheme for image texture classification based on binary local structure pattern. Vis Comput 30(11):1223–1232CrossRefGoogle Scholar
  29. 29.
    Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650MathSciNetCrossRefGoogle Scholar
  30. 30.
    Tiwari D, Tyagi V (2016) Dynamic texture recognition: a review. Adv Intell Syst Comput Springer 434:365–373. doi:10.1007/978-81-322-2752-6_36
  31. 31.
    Tiwari D, Tyagi V (2015) Dynamic texture recognition based on completed volume local binary pattern. Multidim Syst Sign Process. doi:10.1007/s11045-015-0319-6 Google Scholar
  32. 32.
    Varma M and Zisserman A (2003) Texture classification: are filter banks necessary? Proc Int Conf Comput Vis Pattern Recognit 691–698Google Scholar
  33. 33.
    Varma M, Zisserrman A (2009) A statistical approach to material classification using image patch exemplars. IEEE Trans Pattern Anal Mach Intell 31(11):2032–2047CrossRefGoogle Scholar
  34. 34.
    Wang Y, Hu S (2015) Exploiting high level feature for dynamic textures recognition. Neurocomputing 154:217–224CrossRefGoogle Scholar
  35. 35.
    Wang Y, Hu S (2015) Chaotic features for dynamic textures recognition. Soft Comput. doi:10.1007/s00500-015-1618-4 Google Scholar
  36. 36.
    Xu Y, Huang S, Ji H, Fermüller C (2012) Scale-space texture description on SIFT-like textons. Comput Vis Image Underst 116:999–1013CrossRefGoogle Scholar
  37. 37.
    Xu Y, Quan Y, Ling H, and Ji H (2011) Dynamic texture classification using dynamic fractal analysis. IEEE Int Conf Comput Vis (ICCV) 1219–1226Google Scholar
  38. 38.
    Zhao G, Ahonen T, Matas J, Pietikäinen M (2012) Rotation-invariant image and video description with local binary pattern features. IEEE Trans Image Process 21(4):1465–1467MathSciNetCrossRefGoogle Scholar
  39. 39.
    Zhao G, Pietikainen M (2007) Dynamic texture recognition using volume local binary patterns. Proc Workshop Dyn Vis WDV 2005/2006 LNCS 4358, 165–177Google Scholar
  40. 40.
    Zhao G, Pietikäinen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Jaypee University of Engineering and TechnologyGunaIndia

Personalised recommendations