Skip to main content
SpringerLink
Log in

Image Compression and Video Segmentation Using Hierarchical Self-Organization

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed network architecture and the lack of representation in hierarchical relations among data. In this paper, both problems are addressed using growing hierarchical self-organizing models. An advantage of these models is due to the hierarchical architecture, which is more flexible in the adaptation process to input data, reflecting inherent hierarchical relations among data. Comparative results are provided for image compression and image segmentation. Experimental results show that the proposed approach is promising for image processing, and the powerful of the hierarchical information provided by the proposed model.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. http://mmc36.informatik.uni-augsburg.de/VSSN06_OSAC

  2. http://www.cvg.cs.rdg.ac.uk/PETS2001/pets2001-dataset.html

  3. http://www.cs.cmu.edu/~yaser/GroundtruthSeq.rar

References

  1. Alahakoon D, Halgamuge S, Srinivasan B (2000) Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Trans Neural Netw 11:601–614

    Article  Google Scholar 

  2. Araujo A, Costa D (2009) Local adaptive receptive field self-organizing map for image color segmentation. Image Vis Comput 27(9):1229–1239

    Article  Google Scholar 

  3. Barbalho J, Duarte A, Neto D, Costa J, Netto M (2001) Hierarchical som applied to image compression. In: Anonymous (ed) Proceedings of international joint conference on neural networks, 2001 IJCNN ’01, vol 1, pp 442–447

  4. Beaulieu J, Goldberg M (1989) Hierarchy in picture segmentation: a stepwise optimization approach. IEEE Trans Pattern Anal Mach Intell 11(2):150–163

    Article  Google Scholar 

  5. Bezdek JC (1981) Pattern recognition with fuzzy objective function algoritms. Plenum Press

  6. Bhandarkar S, Koh J, Suk M (1997) Multiscale image segmentation using a hierarchical self-organizing map. Neurocomputing 14(3):241–272

    Article  Google Scholar 

  7. Chang CH, Pengfei X, Xiao R, Srikanthan T (2005) New adaptive color quantization method based on self-organizing maps. IEEE Trans Neural Netw 16(1):237–249

    Article  Google Scholar 

  8. Dittenbach M, Rauber A, Merkl D (2001) Recent advances with the growing hierarchical self-organizing map. In: 3rd workshop on self-organising maps (WSOM), pp 140–145

  9. Dong G, Xie M (2005) Color clustering and learning for image segmentation based on neural networks. IEEE Trans Neural Netw 16(4):925–936

    Article  Google Scholar 

  10. Fan J, Yau D, Elmagarmid A, Aref W (2001) Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process 10(10):1454–1466

    Article  MATH  Google Scholar 

  11. Hertz J, Krogh A, Palmer R (1991) Introduction to the theory of neural computation. Addison-Wesley

  12. Kanjanawanishkul K, Uyyanonvara B (2005) Novel fast color reduction algorithm for time-constrained applications. J Vis Commun Image Represent 16(3):311–332

    Article  Google Scholar 

  13. Kassim A, Lee W, Zonoobi D (2009) Hierarchical segmentation-based image coding using hybrid quad-binary trees. IEEE Trans Image Process 18(6):1284–1291

    Article  MathSciNet  Google Scholar 

  14. Kohonen T (1982) Self-organized formation of topologically correct feature maps. Bioll Cybern 43(1): 59–69

    Article  MathSciNet  MATH  Google Scholar 

  15. Lampinen J, Oja E (1992) Clustering properties of hierarchical self-organizing maps. J Math Imaging Vis 2:261

    Article  MATH  Google Scholar 

  16. Lhermitte S, Verbesselt J, Jonckheere I, Nackaerts K, Aardt J, Verstraeten W, Coppin P (2008) Hierarchical image segmentation based on similarity of ndvi time series. Remote Sens Environ 112(2): 506–521. Soil Moisture Exp 2004 (SMEX04) Special Issue

    Google Scholar 

  17. Linde Y, Buzo A, Gray R (1980) An algorithm for vector quantizer design. IEEE Trans Commun 28(1): 84–95

    Article  Google Scholar 

  18. Lopez-Rubio E, Luque-Baena RM, Domínguez E (2011) Foreground detection in video sequences with probabilistic self-organizing maps. Int J Neural Syst 21(3):225–246

    Article  Google Scholar 

  19. Luque RM, Domínguez E, Palomo EJ, Muñoz J (2008) A neural network approach for video object segmentation in traffic surveillance. In: Springer (ed) Lecture Notes in Computer Science, vol 5112, pp 151–158

  20. Maddalena L, Petrosino A (2008) A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans Image Process 17(7):1168–1177

    Article  MathSciNet  Google Scholar 

  21. Ohkura K, Nishizawa H, Obi T, Hasegawa A, Yamaguchi M, Ohyama N (2000) Unsupervised image segmentation using hierarchical clustering. Optical Review

  22. Palomo EJ, Domínguez E, Luque RM, Muñoz J (2009) Image hierarchical segmentation based on a ghsom. In: Leung C, Lee M, Chan J (eds) Neural information processing, Lecture Notes in Computer Science, vol 5863. Springer, Berlin, pp 743–750

  23. Palomo EJ, Domínguez E, Luque RM, Muñoz J (2011) Lossy image compression using a ghsom. In: Proceedings of the 11th international conference on artificial neural networks conference on advances in computational intelligence—volume Part II, IWANN’11, pp 1–8 Springer-Verlag, Berlin, Heidelberg

  24. Pan Y, Birdwell J, Djouadi S (2009) Preferential image segmentation using trees of shapes. IEEE Trans Image Process 18(4):854–866

    Article  MathSciNet  Google Scholar 

  25. Rauber A, Merkl D, Dittenbach M (2002) The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data. IEEE Trans Neural Netw 13(6):1331–1341

    Article  Google Scholar 

  26. Sharon E, Galun M, Sharon D, Basri R, Brandt A (2006) Hierarchy and adaptivity in segmenting visual scenes. Nature. doi:10.1038/nature04977

    Google Scholar 

  27. Wei Y, Fritts J, Sun F (2002) A hierarchical image segmentation algorithm. In: IEEE International Conference on Multimedia and Expo, vol 2, pp 221–224

  28. Xiaojun D, Bui T (2006) A new hierarchical image segmentation method. In: 18th international conference on pattern recognition, pp 108–112

  29. Zhouyu F, Robles-Kelly A (2008) A fast hierarchical approach to image segmentation. In: 19th international conference on pattern recognition, pp 1–4

Download references

Acknowledgments

This work is partially supported by the Ministry of Economy and Competitiveness of Spain under grant TIN2011-24141, project name Detection of anomalous activities in video sequences by self-organizing neural systems, and the Autonomous Government of Andalusia (Spain) under project P12-TIC-6213, project name Development of Self-Organizing Neural Networks for Information Technologies.

Author information

Authors and Affiliations

  1. Department of Computer Science, E.T.S.I. Informatica, University of Malaga, Bulevar Louis Pasteur n 35, 29071 , Malaga, Spain

    Esteban J. Palomo, Enrique Domínguez, Rafael M. Luque-Baena & José Muñoz

Authors
  1. Esteban J. Palomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enrique Domínguez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rafael M. Luque-Baena
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José Muñoz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Esteban J. Palomo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Palomo, E.J., Domínguez, E., Luque-Baena, R.M. et al. Image Compression and Video Segmentation Using Hierarchical Self-Organization. Neural Process Lett 37, 69–87 (2013). https://doi.org/10.1007/s11063-012-9266-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-012-9266-5

Keywords

Search

Navigation