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Dynamic Texture Representation Based on Hierarchical Local Patterns

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12002))

Abstract

A novel effective operator, named HIerarchical LOcal Pattern (HILOP), is proposed to efficiently exploit relationships of local neighbors at a pair of adjacent hierarchical regions which are located around a center pixel of a textural image. Instead of being thresholded by the value of the central pixel as usual, the gray-scale of a local neighbor in a hierarchical area is compared to that of all neighbors in the other region. In order to capture shape and motion cues for dynamic texture (DT) representation, HILOP is taken into account investigating hierarchical relationships in plane-images of a DT sequence. The obtained histograms are then concatenated to form a robust descriptor with high performance for DT classification task. Experimental results on various benchmark datasets have validated the interest of our proposal.

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Notes

  1. 1.

    https://www.csie.ntu.edu.tw/~cjlin/liblinear.

References

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

    Article  Google Scholar 

  2. Arashloo, S.R., Amirani, M.C., Noroozi, A.: Dynamic texture representation using a deep multi-scale convolutional network. JVCIR 43, 89–97 (2017)

    Google Scholar 

  3. Arashloo, S.R., Kittler, J.: Dynamic texture recognition using multiscale binarized statistical image features. IEEE Trans. Multimed. 16(8), 2099–2109 (2014)

    Article  Google Scholar 

  4. Chan, A.B., Vasconcelos, N.: Classifying video with kernel dynamic textures. In: CVPR, pp. 1–6 (2007)

    Google Scholar 

  5. Baktashmotlagh, M., Harandi, M.T., Lovell, B.C., Salzmann, M.: Discriminative non-linear stationary subspace analysis for video classification. IEEE Trans. PAMI 36(12), 2353–2366 (2014)

    Article  Google Scholar 

  6. Barmpoutis, P., Dimitropoulos, K., Grammalidis, N.: Smoke detection using spatio-temporal analysis, motion modeling and dynamic texture recognition. In: EUSIPCO, pp. 1078–1082 (2014)

    Google Scholar 

  7. Chan, A.B., Vasconcelos, N.: Modeling, clustering, and segmenting video with mixtures of dynamic textures. IEEE Trans. PAMI 30(5), 909–926 (2008)

    Article  Google Scholar 

  8. Dubois, S., Péteri, R., Ménard, M.: Characterization and recognition of dynamic textures based on the 2D+T curvelet transform. Signal Image Video Process. 9(4), 819–830 (2015)

    Article  Google Scholar 

  9. Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: LIBLINEAR: a library for large linear classification. JMLR 9, 1871–1874 (2008)

    MATH  Google Scholar 

  10. Fazekas, S., Chetverikov, D.: Analysis and performance evaluation of optical flow features for dynamic texture recognition. Signal Process. Image Commun. 22(7–8), 680–691 (2007)

    Article  Google Scholar 

  11. Garrigues, M., Manzanera, A., Bernard, T.M.: Video extruder: a semi-dense point tracker for extracting beams of trajectories in real time. J. R. Time IP 11(4), 785–798 (2016)

    Google Scholar 

  12. Ghanem, B., Ahuja, N.: Maximum margin distance learning for dynamic texture recognition. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 223–236. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15552-9_17

    Chapter  Google Scholar 

  13. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. IP 19(6), 1657–1663 (2010)

    MathSciNet  MATH  Google Scholar 

  14. Hong, S., Ryu, J., Im, W., Yang, H.S.: D3: recognizing dynamic scenes with deep dual descriptor based on key frames and key segments. Neurocomputing 273, 611–621 (2018)

    Article  Google Scholar 

  15. Ji, H., Yang, X., Ling, H., Xu, Y.: Wavelet domain multifractal analysis for static and dynamic texture classification. IEEE Trans. IP 22(1), 286–299 (2013)

    MathSciNet  MATH  Google Scholar 

  16. Liu, L., Zhao, L., Long, Y., Kuang, G., Fieguth, P.W.: Extended local binary patterns for texture classification. Image Vis. Comput. 30(2), 86–99 (2012)

    Article  Google Scholar 

  17. Mumtaz, A., Coviello, E., Lanckriet, G.R.G., Chan, A.B.: Clustering dynamic textures with the hierarchical EM algorithm for modeling video. IEEE Trans. PAMI 35(7), 1606–1621 (2013)

    Article  Google Scholar 

  18. Nguyen, T.P., Manzanera, A., Garrigues, M., Vu, N.: Spatial motion patterns: action models from semi-dense trajectories. IJPRAI 28(7), 1460011 (2014)

    Google Scholar 

  19. Nguyen, T.P., Manzanera, A., Kropatsch, W.G., N’Guyen, X.S.: Topological attribute patterns for texture recognition. Pattern Recognit. Lett. 80, 91–97 (2016)

    Article  Google Scholar 

  20. Nguyen, T.P., Vu, N., Manzanera, A.: Statistical binary patterns for rotational invariant texture classification. Neurocomputing 173, 1565–1577 (2016)

    Article  Google Scholar 

  21. Nguyen, T.T., Nguyen, T.P., Bouchara, F.: Completed local structure patterns on three orthogonal planes for dynamic texture recognition. In: IPTA, pp. 1–6 (2017)

    Google Scholar 

  22. Nguyen, T.T., Nguyen, T.P., Bouchara, F.: Completed statistical adaptive patterns on three orthogonal planes for recognition of dynamic textures and scenes. J. Electron. Imaging 27(05), 053044 (2018)

    Article  Google Scholar 

  23. Nguyen, T.T., Nguyen, T.P., Bouchara, F.: Smooth-invariant gaussian features for dynamic texture recognition. In: ICIP, pp. 4400–4404 (2019)

    Google Scholar 

  24. Nguyen, T.T., Nguyen, T.P., Bouchara, F., Nguyen, X.S.: Directional beams of dense trajectories for dynamic texture recognition. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2018. LNCS, vol. 11182, pp. 74–86. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01449-0_7

    Chapter  Google Scholar 

  25. Nguyen, T.T., Nguyen, T.P., Bouchara, F., Nguyen, X.S.: Momental directional patterns for dynamic texture recognition. CVIU (2020, in press). https://doi.org/10.1016/j.cviu.2019.102882

  26. Nguyen, T.T., Nguyen, T.P., Bouchara, F., Vu, N.-S.: Volumes of blurred-invariant gaussians for dynamic texture classification. In: Vento, M., Percannella, G. (eds.) CAIP 2019. LNCS, vol. 11678, pp. 155–167. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29888-3_13

    Chapter  Google Scholar 

  27. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. PAMI 24(7), 971–987 (2002)

    Article  Google Scholar 

  28. Peh, C., Cheong, L.F.: Synergizing spatial and temporal texture. IEEE Trans. IP 11(10), 1179–1191 (2002)

    MathSciNet  Google Scholar 

  29. Péteri, R., Fazekas, S., Huiskes, M.J.: Dyntex: a comprehensive database of dynamic textures. Pattern Recognit. Lett. 31(12), 1627–1632 (2010)

    Article  Google Scholar 

  30. Péteri, R., Chetverikov, D.: Dynamic texture recognition using normal flow and texture regularity. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 223–230. Springer, Heidelberg (2005). https://doi.org/10.1007/11492542_28

    Chapter  Google Scholar 

  31. Qi, X., Li, C.G., Zhao, G., Hong, X., Pietikainen, M.: Dynamic texture and scene classification by transferring deep image features. Neurocomputing 171, 1230–1241 (2016)

    Article  Google Scholar 

  32. Qiao, Y., Xing, Z.: Dynamic texture classification using multivariate hidden Markov model. IEICE Trans. 101–A(1), 302–305 (2018)

    Article  Google Scholar 

  33. Quan, Y., Bao, C., Ji, H.: Equiangular kernel dictionary learning with applications to dynamic texture analysis. In: CVPR, pp. 308–316 (2016)

    Google Scholar 

  34. Quan, Y., Huang, Y., Ji, H.: Dynamic texture recognition via orthogonal tensor dictionary learning. In: ICCV, pp. 73–81 (2015)

    Google Scholar 

  35. Quan, Y., Sun, Y., Xu, Y.: Spatiotemporal lacunarity spectrum for dynamic texture classification. CVIU 165, 85–96 (2017)

    Google Scholar 

  36. Ren, J., Jiang, X., Yuan, J., Wang, G.: Optimizing LBP structure for visual recognition using binary quadratic programming. SPL 21(11), 1346–1350 (2014)

    Google Scholar 

  37. Rivera, A.R., Chae, O.: Spatiotemporal directional number transitional graph for dynamic texture recognition. IEEE Trans. PAMI 37(10), 2146–2152 (2015)

    Article  Google Scholar 

  38. Saisan, P., Doretto, G., Wu, Y.N., Soatto, S.: Dynamic texture recognition. In: CVPR, pp. 58–63 (2001)

    Google Scholar 

  39. Tiwari, D., Tyagi, V.: Dynamic texture recognition based on completed volume local binary pattern. MSSP 27(2), 563–575 (2016)

    Google Scholar 

  40. Tiwari, D., Tyagi, V.: A novel scheme based on local binary pattern for dynamic texture recognition. CVIU 150, 58–65 (2016)

    Google Scholar 

  41. Tiwari, D., Tyagi, V.: Improved weber’s law based local binary pattern for dynamic texture recognition. Multimed. Tools Appl. 76(5), 6623–6640 (2017)

    Article  Google Scholar 

  42. Tiwari, D., Tyagi, V.: Dynamic texture recognition using multiresolution edge-weighted local structure pattern. Comput. Electr. Eng. 62, 485–498 (2017)

    Article  Google Scholar 

  43. Wang, H., Kläser, A., Schmid, C., Liu, C.: Dense trajectories and motion boundary descriptors for action recognition. IJCV 103(1), 60–79 (2013)

    Article  MathSciNet  Google Scholar 

  44. Wang, Y., Hu, S.: Chaotic features for dynamic textures recognition. Soft Comput. 20(5), 1977–1989 (2016)

    Article  Google Scholar 

  45. Xu, Y., Huang, S.B., Ji, H., Fermüller, C.: Scale-space texture description on sift-like textons. CVIU 116(9), 999–1013 (2012)

    Google Scholar 

  46. Xu, Y., Quan, Y., Zhang, Z., Ling, H., Ji, H.: Classifying dynamic textures via spatiotemporal fractal analysis. Pattern Recognit. 48(10), 3239–3248 (2015)

    Article  Google Scholar 

  47. Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans. PAMI 29(6), 915–928 (2007)

    Article  Google Scholar 

  48. Zhao, X., Lin, Y., Heikkilä, J.: Dynamic texture recognition using volume local binary count patterns with an application to 2D face spoofing detection. IEEE Trans. Multimed. 20(3), 552–566 (2018)

    Article  Google Scholar 

  49. Zhao, Y., Huang, D.S., Jia, W.: Completed local binary count for rotation invariant texture classification. IEEE Trans. IP 21(10), 4492–4497 (2012)

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Université de Toulon, Aix Marseille Université, CNRS, LIS, Marseille, France

    Thanh Tuan Nguyen, Thanh Phuong Nguyen & Frédéric Bouchara

  2. Faculty of IT, HCMC University of Technology and Education, HCM City, Vietnam

    Thanh Tuan Nguyen

Authors
  1. Thanh Tuan Nguyen
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  2. Thanh Phuong Nguyen
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  3. Frédéric Bouchara
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Corresponding author

Correspondence to Thanh Tuan Nguyen .

Editor information

Editors and Affiliations

  1. DGA, Paris, France

    Jacques Blanc-Talon

  2. University of Auckland, Auckland, New Zealand

    Patrice Delmas

  3. Ghent University, Ghent, Belgium

    Wilfried Philips

  4. CSIRO, Canberra, Australia

    Dan Popescu

  5. University of Antwerp, Wilrijk, Belgium

    Paul Scheunders

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Nguyen, T.T., Nguyen, T.P., Bouchara, F. (2020). Dynamic Texture Representation Based on Hierarchical Local Patterns. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_24

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  • DOI: https://doi.org/10.1007/978-3-030-40605-9_24

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-40605-9

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