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Motion-Based Analysis of Dynamic Textures – A Survey

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Advances in Computing Systems and Applications (CSA 2018)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 50))

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Abstract

Textures such as grass, trees, mountains, buildings and others occupy large spaces of our visual environment. Numerous researches have been devoted to automatically analyze and characterize textures, where static textures found in single images were the first to be studied. Subsequently, this notion was extended to temporal dimension, known as dynamic texture representing variable properties in time such as flames, swaying trees, moving clouds, crowds in public places and even shadows, etc. Lately, temporal texture research is gaining a lot of attention, due to its importance as an effective component in the interpretation of video content.

This paper presents a research survey that focuses on a very captivating subject: Dynamic texture analysis, characterization and recognition. Its motivation is to give an overview of the most up-to-date analysis approaches that have been proposed to characterize then recognize temporal textures in different fields.

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Notes

  1. 1.

    http://alumni.media.mit.edu/~szummer/icip-96.

  2. 2.

    http://www.bernardghanem.com/datasets.

  3. 3.

    http://projects.cwi.nl/dyntex/index.html.

References

  1. Nelson, R.C., Polana, R.: Qualitative recognition of motion using temporal texture. CVGIP: Image Underst. 56(1), 78–89 (1992)

    Google Scholar 

  2. Peh, C.H., Cheong, L.F.: Exploring video content in extended spatio-temporal textures. In: In 1st European workshop on Content-Based Multimedia Indexing, Toulouse, France, pp. 147–153 (1999)

    Google Scholar 

  3. Saisan, P., Doretto, G., Wu, Y.N., Soatto, S.: Dynamic texture recognition. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2001, vol. 2, pp. 58–63 (2001)

    Google Scholar 

  4. Bar-Joseph, Z., El-Yaniv, R., Lischinski, D., Werman, M.: Texture mixing and texture movie synthesis using statistical learning. IEEE Trans. Vis. Comput. Graph. 7(2), 120–135 (2001)

    Article  Google Scholar 

  5. Wang, Y., Zhu, S.C.: Modeling textured motion : particle, wave and sketch. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 213–220, October 2003

    Google Scholar 

  6. Dubois, S., Peteri, R., Menard, M.: Decomposition of dynamic textures using morphological component analysis. IEEE Trans. Circuits Syst. Video Technol. 22(2), 188–201 (2012)

    Article  Google Scholar 

  7. Chetverikov, D., Peteri, R.: A brief survey of dynamic texture description and recognition. In: Proceedings of International Conference on Computer Recognition Systems, pp. 17–26. Springer, Heidelberg (2005)

    Google Scholar 

  8. Rahman, A., Murshed, M.: Temporal Texture Characterization: A Review, pp. 291–316. Springer, Heidelberg (2008)

    Google Scholar 

  9. Mocofan, M., Vasiu, R.: Dynamic textures indexing using the co-occurrence matrix features. In: Proceedings of 2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), pp. 327–330, May 2012

    Google Scholar 

  10. Dubois, S., Peteri, R., Ménard, M.: A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition, pp. 314–321. Springer, Heidelberg (2009)

    Google Scholar 

  11. Smith, J.R., Lin, C.Y., Naphade, M.: Video texture indexing using spatio-temporal wavelets. In: Proceedings of International Conference on Image Processing, vol. 2, pp. 437–440 (2002)

    Google Scholar 

  12. Bouthemy, P., Fablet, R.: Motion characterization from temporal co-occurrences of local motion-based measures for video indexing. In: Proceedings of Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), vol. 1, pp. 905–908, August 1998

    Google Scholar 

  13. Phillips, W., Shah, M., Lobo, N.D.V.: Flame recognition in video. In: Proceedings of Fifth IEEE Workshop on Applications of Computer Vision, pp. 224–229 (2000)

    Google Scholar 

  14. Günay, O.: Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection. Ph.D. thesis, BIlkent university (2009)

    Google Scholar 

  15. Ma, Y., Cisar, P.: Event detection using local binary pattern based dynamic textures. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2009 (CVPR Workshops 2009), pp. 38–44. IEEE (2009)

    Google Scholar 

  16. Komulainen, J., Hadid, A., Pietikäinen, M.: Face spoofing detection using dynamic texture. In: Asian Conference on Computer Vision, pp. 146–157. Springer, Heidelberg (2012)

    Google Scholar 

  17. Hsu, W.L., Chen, T.H.: People gathering recognition based on dynamic texture detection. In: 2015 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, pp. 334–339. IEEE (2015)

    Google Scholar 

  18. Li, J., Chen, L., Cai, Y.: Dynamic texture segmentation using 3-D fourier transform. In: Fifth International Conference on Image and Graphics ICIG 2009, pp. 293–298. IEEE (2009)

    Google Scholar 

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

    Article  Google Scholar 

  20. Amiaz, T., Fazekas, S., Chetverikov, D., Kiryati, N.: Detecting regions of dynamic texture. In: International Conference on Scale Space and Variational Methods in Computer Vision, pp. 848–859. Springer, Heidelberg (2007)

    Google Scholar 

  21. Candes, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Model. Simul. 5(3), 861–899 (2006)

    Article  MathSciNet  Google Scholar 

  22. Lin, L., Xu, Y., Liang, X., Lai, J.: Complex background subtraction by pursuing dynamic spatio-temporal models. IEEE Trans. Image Process. 23(7), 3191–3202 (2014)

    Article  MathSciNet  Google Scholar 

  23. Ali, I., Mille, J., Tougne, L.: Space-time spectral model for object detection in dynamic textured background. Pattern Recogn. Lett. 33(13), 1710–1716 (2012)

    Article  Google Scholar 

  24. Chan, A.B., Mahadevan, V., Vasconcelos, N.: Generalized stauffer-grimson background subtraction for dynamic scenes. Mach. Vis. Appl. 22(5), 751–766 (2011)

    Article  Google Scholar 

  25. Zhang, S., Yao, H., Liu, S.: Dynamic background modeling and subtraction using spatio-temporal local binary patterns. In: Proceedings of 15th IEEE International Conference on Image Processing 2008 (ICIP 2008), pp. 1556–1559. IEEE (2008)

    Google Scholar 

  26. Ramesh, V., et al.: Background modeling and subtraction of dynamic scenes. In: Proceedings of Ninth IEEE International Conference on Computer Vision, pp. 1305–1312. IEEE (2003)

    Google Scholar 

  27. Tesfaldet, M., Brubaker, M.A., Derpanis, K.G.: Two-stream convolutional networks for dynamic texture synthesis. arXiv preprint arXiv:1706.06982 (2017)

  28. Funke, C.M., Gatys, L.A., Ecker, A.S., Bethge, M.: Synthesising dynamic textures using convolutional neural networks. arXiv preprint arXiv:1702.07006 (2017)

  29. Zhu, Z., You, X., Yu, S., Zou, J., Zhao, H.: Dynamic texture modeling and synthesis using multi-kernel gaussian process dynamic model. Sig. Process. 124, 63–71 (2016)

    Article  Google Scholar 

  30. Lizarraga-Morales, R.A., Guo, Y., Zhao, G., Pietikäinen, M., Sanchez-Yanez, R.E.: Local spatiotemporal features for dynamic texture synthesis. EURASIP J. Image Video Process. 2014(1), 17 (2014)

    Article  Google Scholar 

  31. Sheikh, Y., Haering, N., Shah, M.: Shape from dynamic texture for planes. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2285–2292. IEEE (2006)

    Google Scholar 

  32. Polana, R., Nelson, R.: Temporal Texture and Activity Recognition (eds.), pp. 87–124. Springer, Dordrecht (1997)

    Google Scholar 

  33. Fable, R., Bouthemy, P.: Motion-Based Feature Extraction and Ascendant Hierarchical Classification for Video Indexing and Retrieval, pp. 221–229. Springer, Heidelberg (1999)

    Google Scholar 

  34. Fablet, R., Bouthemy, P., Perez, P.: Nonparametric motion characterization using causal probabilistic models for video indexing and retrieval. IEEE Trans. Image Process. 11(4), 393–407 (2002)

    Article  Google Scholar 

  35. Fablet, R., Bouthemy, P.: Motion recognition using spatio-temporal random walks in sequence of 2D motion-related measurements. In: Proceedings of 2001 International Conference on Image Processing (Cat. No.01CH37205), vol. 3, pp. 652–655 (2001)

    Google Scholar 

  36. Fablet, R., Bouthemy, P.: Motion recognition using nonparametric image motion models estimated from temporal and multiscale co-occurrence statistics. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1619–1624 (2003)

    Article  Google Scholar 

  37. Lu, Z., Xie, W., Pei, J., Huang, J.: Dynamic texture recognition by spatio-temporal multiresolution histograms. In: Application of Computer Vision, WACV/MOTIONS 2005 Volume 1. Seventh IEEE Workshops on, vol. 2, pp. 241–246, January 2005

    Google Scholar 

  38. Rahman, A., Murshed, M.: Real-time temporal texture characterization using block-based motion co-occurrence statistics. In: 2004 International Conference on Image Processing ICIP 2004, vol. 3, pp. 1593–1596, October 2004

    Google Scholar 

  39. Rahman, A., Murshed, M., Dooley, L.S.: Feature weighting methods for abstract features applicable to motion based video indexing. In: Proceedings of International Conference on Information Technology: Coding and Computing ITCC 2004, vol. 1, pp. 676–680, April 2004

    Google Scholar 

  40. Rahman, A., Murshed, M.: A temporal texture characterization technique using block-based approximated motion measure. IEEE Trans. Circuits Syst. Video Technol. 17(10), 1370–1382 (2007)

    Article  Google Scholar 

  41. Rahman, A., Murshed, M.: Detection of multiple dynamic textures using feature space mapping. IEEE Trans. Circuits Syst. Video Technol. 19(5), 766–771 (2009)

    Article  Google Scholar 

  42. Fazekas, S., Chetverikov, D.: Dynamic texture recognition using optical flow features and temporal periodicity. In: 2007 International Workshop on Content-Based Multimedia Indexing, pp. 25–32, June 2007

    Google Scholar 

  43. Fazekas, S., Chetverikov, D.: A non-regular optical flow for dynamic textures. In: Fazekas, A., Hajdu, A. (eds.) KÉPAF 2007 6th conference of Hungarian Association for Image Processing and Pattern Recognition. Debrecen, KÉPAF Társ, pp. 157–164 (2007)

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  46. Péteri, R., Chetverikov, D.: Dynamic texture recognition using normal flow and texture regularity. In: Marques, J.S., de la Blanca, N.P., Pina, P. (eds.) Pattern Recognition and Image Analysis: Second Iberian Conference, pp. 223–230. Springer, Heidelberg (2005)

    Google Scholar 

  47. Péteri, R., Chetverikov, D.: Qualitative characterization of dynamic textures for video retrieval. In: Wojciechowski, K., Smolka, B., Palus, H., Kozera, R.S., Skarbek, W., Noakes, L. (eds.) Computer Vision and Graphics: International Conference, ICCVG 2004, Proceedings, Warsaw, Poland, September 2004, pp. 33–38. Springer, Dordrecht (2006)

    Google Scholar 

  48. Péteri, R.: Tracking dynamic textures using a particle filter driven by intrinsic motion information. Mach. Vis. Appl. 22(5), 781–789 (2011)

    Article  Google Scholar 

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Authors and Affiliations

  1. Laboratory for Research in Artificial Intelligence (LRIA), Department of Computer Science, University of Science and Technology Houari Boumediene (USTHB), Bab-Ezzouar, Algiers, Algeria

    Ikram Bida & Saliha Aouat

Authors
  1. Ikram Bida
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  2. Saliha Aouat
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Correspondence to Ikram Bida .

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Editors and Affiliations

  1. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Oualid Demigha

  2. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Badis Djamaa

  3. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Abdenour Amamra

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Bida, I., Aouat, S. (2019). Motion-Based Analysis of Dynamic Textures – A Survey. In: Demigha, O., Djamaa, B., Amamra, A. (eds) Advances in Computing Systems and Applications. CSA 2018. Lecture Notes in Networks and Systems, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-98352-3_20

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