Skip to main content
SpringerLink
Log in

Incremental Learning of the Model for Watershed-Based Image Segmentation

  • Conference paper
Combinatorial Image Analaysis (IWCIA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7655))

Included in the following conference series:

Abstract

Many image analysis methods need a lot of parameters that have to be adjusted to the particular image in order to achieve the best results. Therefore, methods for parameter learning are required that can assist a system developer in building a model. This task is usually called meta-learning. We consider meta-learning for learning the image segmentation parameters so that the image segmenter can be applied to a wide range of images while achieving good image segmentation quality. The meta-learner is based on case-based reasoning. The cases in the case base are comprised of an image description and the solutions that are the associated parameters for the image segmenter. First, the image description is calculated from a new image. The image description is used to index the case base. The closest case is retrieved based on a similarity measure. Then the associated segmentation parameters are given to the image segmenter and the actual image is segmented. We explain the architecture of such a case-based reasoning image segmenter. The case-description as well as the similarity function are described. Finally, we give results on the image segmentation quality.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Attig, A., Perner, P.: A study on the case image description for learning the model of the watershed segmentation. Transactions on Case-Based Reasoning 2(1), 41–54 (2009)

    Google Scholar 

  2. Frucci, M.: Oversegmentation reduction by flooding regions and digging watershed lines. International Journal of Pattern Recognition and Artificial Intelligence 20(1), 15–38 (2006)

    Article  Google Scholar 

  3. Frucci, M., Perner, P., Sanniti di Baja, G.: Case-based reasoning for image segmentation by watershed transformation. In: Perner, P. (ed.) Case-Based Reasoning on Images and Signals. SCI, vol. 73, pp. 319–353. Springer, Berlin (2008)

    Chapter  Google Scholar 

  4. Frucci, M., Perner, P., Sanniti di Baja, G.: Case-based reasoning for image segmentation. International Journal of Pattern Recognition and Artificial Intelligence 22(5), 1–14 (2008)

    Article  Google Scholar 

  5. Frucci, M., di Baja, G.S.: A New Algorithm for Image Segmentation via Watershed Transformation. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011, Part II. LNCS, vol. 6979, pp. 168–177. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  6. Jobin Christ, M.C., Parvathi, R.M.S.: Segmentation of medical image using K-means clustering and marker controlled watershed algorithm. European Journal of Scientific Research 71(2), 190–194 (2012)

    Google Scholar 

  7. Perner, P.: An architecture for a CBR image segmentation system. Engineering Applications of Artificial Intelligence 12(6), 749–759 (1999)

    Article  Google Scholar 

  8. Perner, P.: Why Case-Based Reasoning Is Attractive for Image Interpretation. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 27–44. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  9. Perner, P.: Case-based reasoning for image analysis and interpretation. In: Chen, C., Wang, P.S.P. (eds.) Handbook on Pattern Recognition and Computer Vision, 3rd edn., pp. 95–114. World Scientific Publisher (2005)

    Google Scholar 

  10. Perner, P.: Case-based reasoning and the statistical challenges. Journal Quality and Reliability Engineering International 24(6), 705–720 (2008)

    Article  Google Scholar 

  11. Attig, A., Perner, P.: Meta-learning for Image Processing Based on Case-Based Reasoning. In: Bichindaritz, I., Vaidya, S., Jain, A., Jain, L.C. (eds.) Computational Intelligence in Healthcare 4. SCI, vol. 309, pp. 229–264. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  12. Prewitt, J.M.S.: Object enhancement and extraction. In: Lipkin, B.S., Rosenfeld, A. (eds.) Picture Processing and Psychopictorics, pp. 75–149. Academic Press, New York (1970)

    Google Scholar 

  13. Roerdink, J.B.T.M., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41, 187–228 (2001)

    MathSciNet  Google Scholar 

  14. Soares, C., Brazdil, P.B., Kuba, P.: A meta-learning method to select the kernel width in support vector regression. Machine Learning 54, 195–209 (2004)

    Article  MATH  Google Scholar 

  15. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Patt. Anal. Mach. Intell. 13(6), 583–598 (1991)

    Article  Google Scholar 

  16. Wunsch, G.: Systemtheorie. Akademische Verlagsgesellschaft, Leipzig (1975)

    MATH  Google Scholar 

  17. Zanaty, E.A., Afifi, A.: A watershed approach for improving medical image segmentation. Computer Methods in Biomechanics and Biomedical Engineering (forthcoming paper, 2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany

    Anja Attig & Petra Perner

Authors
  1. Anja Attig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petra Perner
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer and Information Sciences, State University of New York at Fredonia, 280 Central Ave, 14063, Fredonia, NY, USA

    Reneta P. Barneva

  2. Mathematics Department, SUNY Buffalo State College, 1300Elmwood Ave, 14222, Buffalo, NY, USA

    Valentin E. Brimkov

  3. Department of Electrical and Computer Engineering, University of Texas at Austin, 1 University Station C0803, 78712-0240 TX, Austin, USA

    Jake K. Aggarwal

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Attig, A., Perner, P. (2012). Incremental Learning of the Model for Watershed-Based Image Segmentation. In: Barneva, R.P., Brimkov, V.E., Aggarwal, J.K. (eds) Combinatorial Image Analaysis. IWCIA 2012. Lecture Notes in Computer Science, vol 7655. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34732-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34732-0_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34731-3

  • Online ISBN: 978-3-642-34732-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation