Skip to main content
SpringerLink
Log in

Model Driven Image Segmentation Using a Genetic Algorithm for Structured Data

  • Conference paper
Hybrid Artificial Intelligence Systems (HAIS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6076))

Included in the following conference series:

  • 1411 Accesses

Abstract

In this paper, a method, integrating efficiently a semantic approach into an image segmentation process, is proposed. A graph based representation is exploited to carry out this knowledge integration. Firstly, a watershed segmentation is roughly performed. From this raw partition into regions an adjacency graph is extracted. A model transformation turns this syntaxic structure into a semantic model. Then the consistence of the computer-generated model is compared to the user-defined model. A genetic algorithm optimizes the region merging mechanism to fit the ground-truth model. The efficiency of our system is assessed on real images.

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. Bauckhage, C., Braun, E., Sagerer, G.: From image features to symbols and vice versa - using graphs to loop data- and model-driven processing in visual assembly recognition. IJPRAI 18(3), 497–517 (2004)

    Google Scholar 

  2. Bunke, H.: On a relation between graph edit distance and maximum common subgraph. Pattern Recogn. Lett. 18(9), 689–694 (1997)

    Article  Google Scholar 

  3. Van Hentenryck, P., Deville, Y., Teng, C.-M.: A generic arc-consistency algorithm and its specializations. Artificial Intelligence 57(2-3), 291–321 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  4. Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis. Probability & Mathematical Statistics (1990), ISBN-10: 0

    Google Scholar 

  5. Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Research Logistic Quarterly 2, 83–97 (1955)

    Article  MathSciNet  MATH  Google Scholar 

  6. Munkres, J.: Algorithms for the assignment and transportation problems. Journal of the Society of Industrial and Applied Mathematics 5(1), 32–38 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  7. Saarinen, K.: Color image segmentation by a watershed algorithm and region adjacency graph processing, pp. 1021–1024 (1994)

    Google Scholar 

  8. Hode, Y.: Constraint satisfaction problem with bilevel constraint: application to interpretation of over-segmented images. Artificial Intelligence, 93 (June 1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. L3I, University of La Rochelle, av M. Crépeau, 17042, La Rochelle Cedex 1, France

    Romain Raveaux & Guillaume Hillairet

Authors
  1. Romain Raveaux
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guillaume Hillairet
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Facultad de informatica UPV/EHU, San Sebastian, Spain

    Manuel Graña Romay  & M. Teresa Garcia Sebastian  & 

  2. Universidad de Salamanca, Spain

    Emilio Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Raveaux, R., Hillairet, G. (2010). Model Driven Image Segmentation Using a Genetic Algorithm for Structured Data. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13769-3_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13768-6

  • Online ISBN: 978-3-642-13769-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation