Skip to main content
SpringerLink
Log in
Book cover

Joint Pattern Recognition Symposium

DAGM 2003: Pattern Recognition pp 338–345Cite as

Hierarchy of Partitions with Dual Graph Contraction

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2781))

Abstract

We present a hierarchical partitioning of images using a pairwise similarity function on a graph-based representation of an image. This function measures the difference along the boundary of two components relative to a measure of differences of component’s internal differences. This definition attempts to encapsulate the intuitive notion of contrast. Two components are merged if there is a low-cost connection between them. Each component’s internal difference is represented by the maximum edge weight of its minimum spanning tree. External differences are the cheapest weight of edges connecting components. We use this idea to find region borders quickly and effortlessly in a bottom-up ’stimulus-driven’ way based on local differences in a specific feature, like as in preattentive vision. The components are merged ignoring the details in regions of high-variability, and preserving the details in low-variability ones.

This paper has been supported by the Austrian Science Fund under grants P14445-MAT and P14662-INF

This is a preview of subscription content, 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   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bister, M., Cornelis, J., Rosenfeld, A.: A critical view of pyramid segmentation algorithms. Pattern Recognition Letters 11(9), 605–617 (1990)

    Article  MATH  Google Scholar 

  2. Boruvka, O.: O jistém problému minimálnim. Práce Mor. Přírodvěd. Spol. v Brně (Acta Societ. Scienc. Natur. Moravicae) (3), 37–58 (1926)

    Google Scholar 

  3. Felzenszwalb, P.F., Huttenlocher, D.P.: Image Segmentation Using Local Variation. In: Proc. of IEEE Conf. on CVPR, pp.98–104 (1998)

    Google Scholar 

  4. Fischer, B., Buhmann, J.M.: Data Resampling for Path Based Clustering. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 206–214. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  5. Guigues, L., Herve, L.M., Cocquerez, J.-P.: The Hierarchy of the Cocoons of a Graph and its Application to Image Segmentation. Patt. Recog. Lett. 24(8), 1059–1066 (2003)

    Article  MATH  Google Scholar 

  6. Haxhimusa, Y., Glantz, R., Saib, M., Langs, G., Kropatsch, W.G.: Logarithmic Tapering Graph Pyramid. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 117–124. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  7. Haxhimusa, Y., Kropatsch, W.G.: Hierarchical Image Partitioning with Dual Graph Contraction. Techical Report PRIP-TR-81,TU Wien, Austria (2003), http://www.prip.tuwien.ac.at

  8. Horowitz, S., Pavlidis, T.: Picture Segmentation by a Tree Traversal Algorithm. J. Assoc. Compt. Math. 2(23), 368–388 (1976)

    Google Scholar 

  9. Jolion, J.-M., Montanvert, A.: The adaptive pyramid, a framework for 2D image analysis. Comp. Vis., Graph., and Im. Process 55(3), 339–348 (1992)

    MATH  Google Scholar 

  10. Jolion, J.-M., Rosenfeld, A.: A Pyramid Framework for Early Vision. Kluwer Acadademic Pub., Dordrecht (1994)

    Google Scholar 

  11. Kovalevsky, V.A.: Finite topology as applied to image analysis. Comp. Vis., Graph., and Imag. Process 46, 141–161 (1989)

    Article  Google Scholar 

  12. Kropatsch, W.G.: Building Irregular Pyramids by Dual Graph Contraction. IEEProc. Vision, Image and Signal Processing 142(6), 366–374 (1995)

    Article  Google Scholar 

  13. Kropatsch, W.G., BenYacoub, S.: A general pyramid segmentation algorithm. In: Intl. Symp. on Opt. Scie., Eng., and Instr., vol. 2826, pp. 216–224. SPIE, San Jose (1996)

    Google Scholar 

  14. Kropatsch, W.G., Leonardis, A., Bischof, H.: Hierarchical, Adaptive and Robust Methods for Image Understanding. Surv. on Math. for Ind. (9), 1–47 (1999)

    Google Scholar 

  15. Meer, P.: Stochastic image pyramids. Computer Vision, Graphics, and Image Processing 45(3), 269–294 (1989)

    Article  Google Scholar 

  16. Meer, P., Mintz, D., Montanvert, A., Rosenfeld, A.: Consensus vision. In: AAAI 1990 Workshop on Qualitative Vision, pp. 111–115 (1990)

    Google Scholar 

  17. Rosenfeld, A. (ed.): Multiresolution Image Processing and Analysis. Springer, Berlin (1984)

    MATH  Google Scholar 

  18. Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. In: Proceedings IEEE Conf. Comp. Vis. and Patt., pp. 731–737 (1997)

    Google Scholar 

  19. Wertheimer, M.: Uber Gestaltheorie. Philosophische Zeitschrift fur Forschung und Aussprache 1, 30–60 (1925)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Pattern Recognition and Image Processing Group 183/2, Institute for Computer Aided Automation, Vienna University of Technology, Austria

    Yll Haxhimusa & Walter Kropatsch

Authors
  1. Yll Haxhimusa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Walter Kropatsch
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Otto-von-Guericke University, 39106, Magdeburg, Germany

    Bernd Michaelis

  2. Institute for Electronics, Signal Processing and Communications (IESK), Otto-von-Guericke-University Magdeburg, P.O. Box 4120, D-39016, Magdeburg, Germany

    Gerald Krell

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Haxhimusa, Y., Kropatsch, W. (2003). Hierarchy of Partitions with Dual Graph Contraction. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-45243-0_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40861-1

  • Online ISBN: 978-3-540-45243-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation