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Structural Graph Extraction from Images

  • Antonio-Javier Gallego-Sánchez
  • Jorge Calera-Rubio
  • Damián López
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 151)

Abstract

We present three new algorithms to model images with graph primitives. Our main goal is to propose algorithms that could lead to a broader use of graphs, especially in pattern recognition tasks. The first method considers the q-tree representation and the neighbourhood of regions. We also propose a method which, given any region of a q-tree, finds its neighbour regions. The second algorithm reduces the image to a structural grid. This grid is postprocessed in order to obtain a directed acyclic graph. The last method takes into account the skeleton of an image to build the graph. It is a natural generalization of similar works on trees [8, 12]. Experiments show encouraging results and prove the usefulness of the proposed models in more advanced tasks, such as syntactic pattern recognition tasks.

Keywords

Directed Acyclic Graph Grid Graph Tree Automaton Location Array Pattern Recognition Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Cychosz, J.M.: Thinning algorithm from the article: Efficient binary image thinning using neighbourhood maps. In: Graphics Gems IV, pp. 465–473. Academic Press (1994)Google Scholar
  2. 2.
    de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational geometry, pp. 291–306. Springer (2000)Google Scholar
  3. 3.
    Escolano, F., Giorgi, D., Hancock, E.R., Lozano, M.A., Falcidieno, B.: Flow Complexity: Fast Polytopal Graph Complexity and 3D Object Clustering. In: Torsello, A., Escolano, F., Brun, L. (eds.) GbRPR 2009. LNCS, vol. 5534, pp. 253–262. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Flasinski, M., Myslinski, S.: On the use of graph parsing for recognition of isolated hand postures of Polish Sign Language. Pattern Recognition 43, 2249–2264 (2010)CrossRefGoogle Scholar
  5. 5.
    Goodchild, M.: Quadtree algorithms and spatial indexes. Technical Issues in GIS, NCGIA, Core Curriculum 37, 5–6 (1990)Google Scholar
  6. 6.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1), 10–18 (2009)CrossRefGoogle Scholar
  7. 7.
    Liu, J., Li, M., Liu, Q., Lu, H., Ma, S.: Image annotation via graph learning. Pattern Recognition 42, 218–228 (2009)zbMATHCrossRefGoogle Scholar
  8. 8.
    López, D., Piñaga, I.: Syntactic Pattern Recognition by Error Correcting Analysis on Tree Automata. In: Amin, A., Pudil, P., Ferri, F., Iñesta, J.M. (eds.) SPR 2000 and SSPR 2000. LNCS, vol. 1876, pp. 133–142. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Luque, R.G., Comba, J.L.D., Freitas, C.: Broad-phase collision detection using semi-adjusting bsp-trees. In: ACM i3D, pp. 179–186 (2005)Google Scholar
  10. 10.
    Newman, M.E.J.: The structure and function of complex networks. SIAM 45 (2003)Google Scholar
  11. 11.
    Poveda, J., Gould, M.: Multidimensional binary indexing for neighbourhood calculations in spatial partition trees. Comput. Geosci. 31(1), 87–97 (2005)CrossRefGoogle Scholar
  12. 12.
    Rico-Juan, J.R., Micó, L.: Comparison of AESA and LAESA search algorithms using string and tree edit distances. Pattern Recognition Letters 24, 1427–1436 (2003)CrossRefGoogle Scholar
  13. 13.
    Shin, H., Tsuda, K., Schölkopf, B.: Protein functional class prediction with a combined graph. Expert Systems with Applications 36, 3284–3292 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Antonio-Javier Gallego-Sánchez
    • 1
  • Jorge Calera-Rubio
    • 1
  • Damián López
    • 2
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversity of AlicanteAlicanteSpain
  2. 2.Departamento de Sistemas Informáticos y ComputaciónTechnical University of ValenciaValenciaSpain

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