Skip to main content
SpringerLink
Log in

Comparative Analysis of Different Clustering Techniques for Video Segmentation

  • Conference paper
  • First Online:
Innovations in Electronics and Communication Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 65))

  • 747 Accesses

Abstract

Video segmentation is an extremely challenging and active area in the field of video processing and computer vision. Video segmentation techniques can be classified basically into two approaches: one approach for which there are preassigned thresholds and another clustering approach for which the number of clusters has been used, which is known. Here, we have studied and analyzed the cluster-based techniques such as mean-shift, K-means, and fuzzy C-means segmentation algorithms. We have evaluated and compared the performances of segmentation methods qualitatively and also quantitatively. To calculate the different quantitative metrics, the images and ground truth of the CDnet 2014 database have been used.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • 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

References

  1. Jiang H, Zhang G, Wang H, Bao H (2015) Spatio-temporal video segmentation of static scenes and its applications. IEEE Trans Multimed 17(1)

    Article  Google Scholar 

  2. Wu GK, Reed TR (1999) Image sequence processing using spatiotemporal segmentation. IEEE Trans Circ Syst Video Technol 9(5):798–807

    Article  Google Scholar 

  3. Kim EY, Hwang SW, Park SH, Kim HJ (2001) Spatiotemporal segmentation using genetic algorithms. Pattern Recognit 34(10):2063–2066

    Article  Google Scholar 

  4. Koprinska I, Carrato S (2001) Temporal video segmentation: a survey. Signal Process Image Commun 16(5):477–500

    Google Scholar 

  5. Megret R, Dementhon D (2002) A survey of spatio-temporal grouping techniques. In: Language and media process, University of Maryland, College Park, MD, USA, Tech. Rep. LAMP-TR-094/CS-TR-4403

    Google Scholar 

  6. Kumar MP, Torr PHS, Zisserman A (2008) Learning layered motion segmentations of video. Int J Comput Vis 76(3):301–319

    Article  Google Scholar 

  7. Shi J, Malik J (1998) Motion segmentation and tracking using normalized cuts. In: Proceedings of the ICCV, pp 1154–1160

    Google Scholar 

  8. Fowlkes C, Belongie S, Malik J (2001) Efficient spatio temporal grouping using the nystrom method. In Proceedings of the CVPR, pp 231–238

    Google Scholar 

  9. Khan S, Shah M (2004) Object based segmentation of video using color, motion and spatial information. In: Proceedings of the CVPR, vol 2, pp 746–750

    Google Scholar 

  10. Remers D, Oatto S (2003) Variational space-time motion segmentation. In: Proceedings of the ICCV, pp 886–893

    Google Scholar 

  11. Itnick CL, Jojic N, Kang SB (2005) Consistent segmentation for optical flow estimation. In Proceedings of the ICCV, vol 2, pp 1308–1315

    Google Scholar 

  12. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  13. Comaniciu D, Meer P, Member S (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  14. Mobahi H, Rao S, Yang AY, Sastry SS, Ma Y (2011) Segmentation of natural images by texture and boundary compression. Int J Comput Vis 95(1):86–98

    Article  Google Scholar 

  15. Sharon E, Galun M, Sharon D, Basri R, Brandt A (2006) Hierarchy and adaptively in segmenting visual scenes. Nature 442(7104):719–846

    Article  Google Scholar 

  16. Lim YW, Lee SU (1990) On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognit 23(9):935–952

    Google Scholar 

  17. Hance GA, Umbaugh SE, Moss RH, Stoecker WV (1996) Unsupervised color image segmentation with application to skin borders. IEEE Eng Med Biol, 104–111

    Google Scholar 

  18. Devikar MM, Jhac MK (2013) Segmentation of images using histogram based FCM clustering algorithm and spatial probability, Department of Telecommunication Engineering, CMRIT, Bangalore, India. Int J Adv Eng Technol

    Google Scholar 

  19. Ali SM, Abood LK, Abdoon RS (2013) Clustering and enhancement methods for extracting 3D brain tumor of MRI images. Remote Sensing Research Unit, Department of Computer Science, University of Baghdad, Department of Physics, University of Babylon, Volume 3, Issue 9

    Google Scholar 

  20. Senior A, Hampapur A, Tian Y, Brown L, Pankanti S, Bolle R (2000) Appearance models for occlusion handling. In: Proceedings of the 2nd IEEE workshop performance evaluation of tracking and surveillance

    Google Scholar 

  21. Erdemand CE, Sankur B (2000) Performance evaluation metrics for object based video segmentation. In: Proceedings of 10th European Signal Processing Conference, vol 2, pp 917–920

    Google Scholar 

  22. Marichal X, Villegas P (2000) Objective evaluation of segmentation masks in video sequences. In: Proceedings of 10th European Signal Processing Conference, vol 4

    Google Scholar 

  23. Wang Y, Jodoin P-M, Porikli F, Konrad J, Benezeth Y, Ishwar P (2014) CDnet 2014: an expanded change detection benchmark dataset. In: Proceedings of the IEEE conference on workshops of computer vision and pattern recognition (CVPR), pp 387–394

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, VSSUT, Burla, Sambalpur, Odisha, India

    Tunirani Nayak & Nilamani Bhoi

Authors
  1. Tunirani Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nilamani Bhoi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tunirani Nayak .

Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Ibrahimpatnam, Telangana, India

    H. S. Saini

  2. Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    R. K. Singh

  3. Indian Institute of Technology Bombay, Mumbai, Maharashtra, India

    Girish Kumar

  4. Department of ECE, NIT Srinagar, Srinagar, Jammu and Kashmir, India

    G.M. Rather

  5. Department of ECE, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Telangana, India

    K. Santhi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nayak, T., Bhoi, N. (2019). Comparative Analysis of Different Clustering Techniques for Video Segmentation. In: Saini, H., Singh, R., Kumar, G., Rather, G., Santhi, K. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-13-3765-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-3765-9_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3764-2

  • Online ISBN: 978-981-13-3765-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation