Dynamic Texture Segmentation Using Texture Descriptors and Optical Flow Techniques | SpringerLink
Skip to main content
Springer Nature Link
Log in

Dynamic Texture Segmentation Using Texture Descriptors and Optical Flow Techniques

  • Conference paper
Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 328))

Abstract

The texture which is in motion is known as Dynamic texture. As the texture can change in shape and direction over time, Segmentation of Dynamic Texture is a challenging task. Furthermore, features of Dynamic texture like spatial (i.e., appearance) and temporal (i.e., motion) may differ from each other. However, studies are mostly limited to characterization of single dynamic textures in the current literature. In this paper, the segmentation problem of image sequences consisting of cluttered dynamic textures is addressed. For the segmentation of dynamic texture, two local texture descriptor based techniques and Lucas-Kanade optical flow technique are combined together to achieve accurate segmentation. Two texture descriptor based techniques are Local binary pattern and Weber local descriptor. These descriptors are used in spatial as well as in temporal domain and it helps to segment a frame of video into distinct regions based on the histogram of the region. Lucas-Kanade based optical flow technique is used in temporal domain, which determines direction of motion of dynamic texture in a sequence. These three features are computed for every section of individual frame and equivalent histograms are obtained. These histograms are concatenated and compared with suitable threshold to obtain segmentation of dynamic texture.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 22879
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 28599
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Doretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic textures. International Journal Computer Vision 51(2), 91–109 (2003)

    Article  MATH  Google Scholar 

  2. Doretto, G., Cremers, D., Favaro, P., Soatto, S.: Dynamic texture segmentation. In: Proc. IEEE International Conference on Computer Vision, pp. 1236–1242 (2003)

    Google Scholar 

  3. Chetverikov, D., Péteri, R.: A brief survey of dynamic texture description and recognition. In: Proc. 4th Int. Conf. Comput. Recognit. Syst., pp. 17–26 (2005)

    Google Scholar 

  4. Zhao, G., Pietikäinen, 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)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Chen, J., Zhao, G., Salo, M., Rahtu, E., Pietikäinen, M.: Automatic Dynamic Texture Segmentation using Local Descriptors and Optical Flow. IEEE Trans. on Image Processing 22(1), 326–339 (2013)

    Article  Google Scholar 

  7. Chen, J., Zhao, G., Pietikäinen, M.: An improved local descriptor and threshold learning for unsupervised dynamic texture segmentation. In: Proc. 12th IEEE Int. Conf. Comput. Vis. Workshop, pp. 460–467 (October 2009)

    Google Scholar 

  8. Vidal, R., Ravichandran, A.: Optical flow estimation & segmentation of multiple moving dynamic textures. In: Proc. IEEE Int. Conf. Comp. Vis. Pattern Reco., pp. 516–521 (2005)

    Google Scholar 

  9. Cooper, L., Liu, J., Huang, K.: Spatial segmentation of temporal texture using mixture linear models. In: Proc. Int. Conf. Dynamical Vis., pp. 142–150 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  11. Chan, A.B., Vasconcelos, N.: Variational layered dynamic textures. In: Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit., pp. 1062–1069 (2009)

    Google Scholar 

  12. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Inter. Joint Conf. on Artificial Intelligence, vol. 3, pp. 674–679 (1981)

    Google Scholar 

  13. Bouguet, J.: Pyramidal Implementation of the Lucas-Kanade Feature Tracker Description of the algorithm. Intel Corporation, Microprocessor Research Labs 1(2), 1–9 (1999)

    Google Scholar 

  14. Pteri, R., Fazekas, S., Huiskes, M.J.: DynTex: A Comprehensive Database of Dynamic Textures. Pattern Recognition Letters 31(12), 1627–1632 (2010), http://rpeteri.free.fr/index.html

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, Maharashtra Institute of Technology, Pune, India

    Pratik Soygaonkar & Shilpa Paygude

  2. Electronics and Telecommunication Engineering Department, College of Engineering, Pune, India

    Vibha Vyas

Authors
  1. Pratik Soygaonkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shilpa Paygude
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vibha Vyas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pratik Soygaonkar .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science Engineering, Anil Neerukonda Ins Tech & Sci Dept of Comp Sci Engg, Vishakapatnam, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Bhubaneswar Engineering College, Bhubaneswar, Odisha, India

    Bhabendra Narayan Biswal

  3. University of Hyderabad, Hyderabad, Andhra Pradesh, India

    Siba K. Udgata

  4. Department of Computer Science and Engineering, University of Kalyanai Faculty of Engg., Tech. & Management, Kalyanai, West Bengal, India

    J. K. Mandal

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Soygaonkar, P., Paygude, S., Vyas, V. (2015). Dynamic Texture Segmentation Using Texture Descriptors and Optical Flow Techniques. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-12012-6_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-12012-6_31

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12011-9

  • Online ISBN: 978-3-319-12012-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation