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Dynamic Texture Segmentation Approaches for Natural and Manmade Cases: Survey and Experimentation

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Abstract

Dynamic Textures are temporal extension of static textures. Texture is defined as an image with some repetitive pattern in it. Dynamic texture (DT) is nothing but video data with some stationary properties and some moving objects in it. DT segmentation is a broad branch of DT Analysis. Segmentation is separating disjoint regions in the image frames which have homogeneous properties such as texture, color, motion. An important role is played by it in the applications like forest fire detection, traffic density detection, crowd congestion detection before stampede, auto pilot airplanes. There are various approaches used for DT segmentation. In this paper, the approaches based on optical flow, local spatiotemporal technique (Local Binary Pattern) and Global spatiotemporal technique (Contourlet transform) are discussed. Each technique is used and modified or combined with some other technique by researchers. This paper gives an overview of all techniques along with variations in them and the benefits achieved by using them on DT dataset. Some experimental results are also presented.

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References

  1. Polana R, Nelson R (1997) Temporal texture and activity recognition. Motion-Based Recognition. Kluwer, Norwell, MA, pp 87–115

    Google Scholar 

  2. Chetverikov D, Peteri R (2005) A brief survey of dynamic texture description and recognition. In: Proceedings of the 4th international conference computer recognition systems, pp 17–26

  3. Doretto G, Cremers D, Favaro P, Soatto S (2003) Dynamic texture segmentation. In: Processing of 9th IEEE international conference on computer vision (ICC’03), vol 2, pp 1236–1242

  4. Doretto G, Chiuso A, Wu YN, Soatto S (2003) Dynamic textures. Int J Comput Vis 51(2):91–109

    Article  Google Scholar 

  5. Vidal R, Ravichandran A (2005) Optical flow estimation and segmentation of multiple moving dynamic textures. In: Proceedings of the IEEE international conference computer vision pattern recognition, pp 516–521

  6. Cooper L, Liu J, Huang K (2005): Spatial segmentation of temporal texture using mixture linear models. In: Proceedings of the international conference on dynamical vision, pp 142–150

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

    Article  Google Scholar 

  8. Chan AB, Vasconcelos N (2009) Variational layered dynamic textures. In: IEEE international conference on computer vision and pattern recognition, pp 1062–1069

  9. Fleet DJ, Weiss Y (2006) Optical flow estimation. In: Handbook of mathematical models in computer vision. Springer, Berlin. ISBN 0-387-26371-3

  10. Vidal R, Ravichandran A (2005) Optical flow estimation & segmentation of multiple moving dynamic textures. In: Proceedings of IEEE international conference computer vision and pattern recognition, pp 516–521

  11. Quan H, Wang C (2009) Overview of dynamic texture recognition techniques based on optical flow. In: IEEE international conference on computational intelligence and software engineering, Wuhan, China

  12. Barron JL, Fleet DJ, Beauchemin S (1994) Performance of optical flow techniques. Int J Comput Vis 12(1):43–77

    Article  Google Scholar 

  13. Horn B, Schunck B (1981) Determining optical flow. Artif Intell 17:185–203

    Article  Google Scholar 

  14. Bruhn A, Weickert J, Schnörr C (2005) Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods. Int J Comput Vis 61(3):211–231

    Article  Google Scholar 

  15. Quan H (1899) A new method of dynamic texture segmentation based on optical flow and level set combination. In: IEEE international conference on information science and engineering, pp 1063–1066

  16. Amiaz T, Fazekas S, Chetverikov D, Kiryati N (2007) Detecting regions of dynamic texture. In: International conference on scale space and variational methods in computer vision, LNCS 4485, pp 848–859

  17. Chetverikov D, Fazekas S, Haindl M (2011) Dynamic texture as foreground and background. Mach Vis Appl 22(5):741–750

    Article  Google Scholar 

  18. Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2010) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720

    Article  Google Scholar 

  19. Chen J, Zhao G, Pietikäinen M (2009) An improved local descriptor and threshold learning for unsupervised dynamic texture segmentation. In: Proceedings of the 12th IEEE international conference on computer vision workshop, pp 460–467

  20. Chen J, Zhao G, MikkoSalo ER, Pietikinen M (2013) Automatic dynamic texture segmentation using local descriptors and optical flow. IEEE Trans Image Process 22(1):326–339

    Article  MathSciNet  Google Scholar 

  21. 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):915–928

    Article  Google Scholar 

  22. Zhao G, Ahonen T, Matas J, Pietikainen M (2012) Rotation-invariant image and video description with local binary pattern features. IEEE Trans Image Process 21(4):1465–1477

    Article  MathSciNet  Google Scholar 

  23. Nanni L, Lumini A, Brahnam S (2010) Local binary patterns variants as texture descriptors for medical image analysis. Artif Intell Med 49(2):117–125

    Article  Google Scholar 

  24. Qian X, Hua X-S, Chen P, Ke L (2011) PLBP: an effective local binary patterns texture descriptor with pyramid representation. Pattern Recognit 44(10–11):2502–2515

    Article  Google Scholar 

  25. Nosaka R, Ohkawa Y, Fukui K (2011) Feature extraction based on co-occurrence of adjacent local binary patterns. In: Advances in image and video technology, pacific rim symposium on image and video technology, PSIVT, pp 82–91

  26. Subrahmanyam M, Maheshwari RP, Balasubramanian R (2012) Local maximum edge binary patterns: a new descriptor for image retrieval and object tracking. Signal Process 92(6):1467–1479

    Article  Google Scholar 

  27. Brahnam S, Jain LC, Nanni L, Lumini A (eds) (2014) Local binary patterns: new variants and applications. Springer, Berlin

    MATH  Google Scholar 

  28. Zhao Y, Jia W, Hu R-X, Min H (2013) Completed robust local binary pattern for texture classification. J Neurocomput 106:68–76

    Article  Google Scholar 

  29. Huang D, Shan C, Ardabilian M, Wang Y, Chen L (2011) Local binary patterns and its application to facial image analysis: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 41(6):765–781

    Article  Google Scholar 

  30. Ahonen T, Matas J, He C, Pietikäinen M (2009) Rotation invariant image description with local binary pattern histogram fourier features. In: IEEE SCIA 2009: image analysis, pp 61–70

    Chapter  Google Scholar 

  31. Liu L, Fieguth P, Guo Y, Wang X, Pietikinen M (2011) Local binary features for texture classification. Pattern Recognit 62(C):135–160

    Google Scholar 

  32. Dubois S, Péteri R, Ménard M (2013) Characterization and recognition of dynamic textures based on the 2D + T curvelet transform. In: Springer signal image and video processing. SIViP, Verlag, London

    Article  Google Scholar 

  33. Candès E (1998) Ridgelets: Theory and applications. Ph.D. thesis, University of Stanford

  34. Donoho D, Duncan M (1999) Digital curvelet transform: strategy, implementation and experiments. In: Wavelet applications VII. SPIE, pp 12–29

  35. Do MN (2001) Directional multiresolution image representation. PhD thesis, Lau same, Switzerland

  36. Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14(12):2091–2106

    Article  Google Scholar 

  37. Cunha AL, Zhou J, Do MN (2006) The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process 15(10):3089–3101

    Article  Google Scholar 

  38. Peteri R, Fazekas S, Huiskes MJ (2010) DynTex: a comprehensive database of dynamic textures. Pattern Recognit Lett 31(12):1627–1632

    Article  Google Scholar 

  39. Lazarg H (2012) Combination of the level-set methods with the Contourlet transform for the segmentation of the IVUS images. Int J Biomed Imag 2012:1

    Article  Google Scholar 

  40. Long Z, Younan NH (2013) Multiscale texture segmentation via a contourlet contextual hidden Markov model. J Dig Sig Process 23(3):859–869

    Article  MathSciNet  Google Scholar 

  41. Zhao H, Zhao X, Zhang T, Liu Y (2017) A new contourlet transform with adaptive directional partitioning. IEEE Signal Process Lett 24(6):843–847

    Article  Google Scholar 

  42. Wang X, Chen W, Gao J, Wang C (2018) Hybrid image denoising method based on non-subsampled contourlet transform and bandelet transform. IET Image Proc 12(5):778–784

    Article  Google Scholar 

  43. Upla KP, Joshi MV, Gajjar PP (2015) An Edge preserving multiresolution fusion: use of contourlet transform and MRF prior. IEEE Trans Geosci Remote Sens 53(6):3210–3220

    Article  Google Scholar 

  44. Devanna H, Satish Kumar GAE, Giriprasad MN (2016) Analysis and applications of modified non-subsampled contourlet transform. In: IEEE international conference on recent trends in engineering, science and technology: (ICRTEST 2016)

  45. Shabanzade F, Ghassemian H (2017) Combination of wavelet and contourlet transforms for PET and MRI image fusion. In: Artificial intelligence and signal processing conference (AISP)

  46. Babu JJJ, Sudha GF (2014) Non-subsampled contourlet transform based image Denoising in ultrasound thyroid images using adaptive binary morphological operations. IET Comput Vision 8(6):718–728

    Article  Google Scholar 

  47. Yongpeng X, Qian Y, Yang F, Li Z, Sheng G, Jiang X (2018) DC cable feature extraction based on the PD image in the non-subsampled contourlet transform domain. IEEE Trans Dielectr Electr Insul 25(2):533–540

    Article  Google Scholar 

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Authors and Affiliations

  1. Maharashtra Institute of Technology, Pune, India

    Shilpa Paygude

  2. College of Engineering, Pune, India

    Vibha Vyas

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  1. Shilpa Paygude
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  2. Vibha Vyas
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Correspondence to Shilpa Paygude.

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Paygude, S., Vyas, V. Dynamic Texture Segmentation Approaches for Natural and Manmade Cases: Survey and Experimentation. Arch Computat Methods Eng 27, 285–297 (2020). https://doi.org/10.1007/s11831-018-09305-9

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  • DOI: https://doi.org/10.1007/s11831-018-09305-9

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