Skip to main content
SpringerLink
Log in
Book cover

International Work-Conference on Artificial Neural Networks

IWANN 2009: Bio-Inspired Systems: Computational and Ambient Intelligence pp 399–406Cite as

Graph-Based Representations in Pattern Recognition and Computational Intelligence

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5517))

Abstract

Graph theory, which used to be a purely academic discipline, is now increasingly becoming an essential part in different areas of research. This paper briefly present new perspectives in graph–based representations applied in emerging fields, such as computer vision and image processing, robotics, network analysis, web mining, chemistry, bioinformatics, sensor networks, biomedical engineering or evolutionary computation.

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   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
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. Cheung, K., Shin, D.: A graph–based meta–data framework for interoperation between genome databases. In: First IEEE Int. Symposium Bioinformatics Biomedical Eng. (2000)

    Google Scholar 

  2. Ion, A., Haxhimusa, Y., Kropatsch, W.: A graph–based concept for spatiotemporal information in cognitive vision. In: Vento, M., Brun, L. (eds.) GbR 2005, pp. 223–232. Springer, Heidelberg (2005)

    Google Scholar 

  3. Ölz, W., Kropatsch, W.: Graph representation of fingerprint topology. In: Computer Vision Winter Workshop, pp. 51–58 (2004)

    Google Scholar 

  4. Pizlo, Z., Stefanov, E., Saalweachter, J., Haxhimusa, Y., Kropatsch, W.: Traveling salesman problem: A foveating pyramid model. The Journal of Problem Solving 1(1), 83–101 (2006)

    Google Scholar 

  5. Kropatsch, W.: Benchmarking graph matching algorithm. Pattern Recognition Letters 24(8), 1051–1059 (2003)

    Article  Google Scholar 

  6. Marfil, R., Molina-Tanco, L., Bandera, A., Rodríguez, J.A., Sandoval, F.: Pyramid segmentation algorithms revisited. Pattern Recognition 39(8), 1430–1451 (2006)

    Article  MATH  Google Scholar 

  7. Zahn, C.: Graph-theoretic methods for detecting and describing gestalt clusters. IEEE Transactions on Computing 20, 68–86 (1971)

    Article  MATH  Google Scholar 

  8. Felzenszwalb, P., Huttenlocher, D.: Image segmentation using local variation. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 98–104 (1998)

    Google Scholar 

  9. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Pattern Analysis Machine Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  10. Maire, M., Arbeláez, P., Fowlkes, C., Malik, J.: Using contours to detect and localize junctions in natural images. In: Int. Conf. Computer Vision Pattern Recognition (2008)

    Google Scholar 

  11. Arbeláez, P., Cohen, L.: A metric approach to vector–valued image segmentation. Int. Journal of Computer Vision 69, 119–126 (2006)

    Article  Google Scholar 

  12. Haxhimusa, Y., Glantz, R., Kropatsch, W.G.: Constructing stochastic pyramids by MIDES - maximal independent directed edge set. In: Hancock, E.R., Vento, M. (eds.) GbRPR 2003. LNCS, vol. 2726, pp. 35–46. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  13. Zivkovic, Z., Bakker, B., Kröse, B.: Hierarchical map building and planning based on graph partitioning. In: Proc. of the IEEE Int. Conf. Robotics and Automation, pp. 803–809 (2006)

    Google Scholar 

  14. Marfil, R., Bandera, A.: Graph abstraction preserving the topology: Application to environment mapping for mobile robotics. In: Computational Algebraic Topology within image context (2008)

    Google Scholar 

  15. Choset, H., Nagatani, K.: Topological simultaneous localisation and mapping: Towards exact localisation without explicit localisation. IEEE Trans. Robotics and Automation 17(2), 125–137 (2001)

    Article  Google Scholar 

  16. Blanco, J., González, J., Fernández-Madrigal, J.: Consistent observation grouping for generating metric–topological maps that improves robot localization. In: IEEE Int. Conf. Robotics Automation, pp. 818–823 (2006)

    Google Scholar 

  17. Blanco, J., Fernández-Madrigal, J., González, J.: A new approach for large–scale localization and mapping: Hybrid metric–topological SLAM. In: IEEE Int. Conf. Robotics Automation, pp. 2061–2067 (2007)

    Google Scholar 

  18. Brunskill, E., Kollar, T., Roy, N.: Topological mapping using spectral clustering and classification. In: IROS 2007, pp. 3491–3496 (2007)

    Google Scholar 

  19. Torsello, A., Hancock, E.R.: Graph Embedding using Tree Edit Union. Pattern Recognition 40, 1393–1405 (2007)

    Article  MATH  Google Scholar 

  20. Luo, B., Wilson, R.C., Hancock, E.R.: Spectral Embedding of Graphs. Pattern Recognition 36, 2213–2223 (2003)

    Article  MATH  Google Scholar 

  21. Qiu, H., Hancock, E.R.: Clustering and Embedding using Commute Times. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1873–1890 (2007)

    Article  Google Scholar 

  22. Fan Zhang, F., Hancock, E.R.: Graph Spectral Image Smoothing using the Heat Kernel. Pattern Recognition 41(11), 3328–3342 (2008)

    Article  MATH  Google Scholar 

  23. Robles-Kelly, A., Hancock, E.R.: A Graph Spectral Approach to Shape-from-shading. IEEE Transactions on Image Processing 13, 912–926 (2004)

    Article  Google Scholar 

  24. Robles-Kelly, A., Hancock, E.R.: A Riemannian Approach to Graph Embedding. Pattern Recognition 40, 1042–1056 (2007)

    Article  MATH  Google Scholar 

  25. Xiao, B., Torsello, A., Hancock, E.R.: Isotree: Tree clustering via Metric Embedding. Neurocomputing 71(10-12), 2029–2036 (2008)

    Article  Google Scholar 

  26. Torsello, A., Hancock, E.R.: Learning Shape-Classes using a Mixture of Tree-unions. IEEE Trans. Pattern Anal. Machine Intell. 28(6), 954–967 (2006)

    Article  Google Scholar 

  27. Torsello, A., Robles-Kelly, A., Hancock, E.R.: Discovering Shape Classes using Tree Edit Distance and Pairwise Clustering. International Journal of Computer Vision 72, 259–285 (2007)

    Article  Google Scholar 

  28. Lozano, M.A., Escolano, F.: Protein Classification by Matching and Clustering Surface Graphs. Pattern Recognition 39(4), 539–551 (2006)

    Article  MATH  Google Scholar 

  29. Jiang, X., Münger, A., Bunke, H.: On Median Graphs: Properties, Algorithms, and Applications. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1144–1151 (2001)

    Article  Google Scholar 

  30. Ferrer, M., Valveny, E., Serratosa, F., Bunke, H.: Exact Median Graph Computation Via Graph Embedding. In: SSPR/SPR 2008, pp. 15–24 (2008)

    Google Scholar 

  31. Escolano, F., Bonev, B., Suau, P., Aguilar, W., Frauel, Y., Sáez, J.M., Cazorla, M.: Contextual Visual Localization: Cascaded Submap Classification, Optimized Saliency Detection, and Fast View Matching. In: IROS 2007, pp. 1715–1722 (2007)

    Google Scholar 

  32. Gold, S., Rangarajan, A.: A Graduated Assignment Algorithm for Graph Matching. IEEE Trans. Pattern. Anal. Mach. Intell. 18(4), 377–388 (1996)

    Article  Google Scholar 

  33. Pelillo, M.: Replicator Equations, Maximal Cliques, and Graph Isomorphism. Neural Comput. 11, 1933–1955 (1999)

    Article  Google Scholar 

  34. Luo, B., Hancock, E.R.: Structural Graph Matching using the EM Algorithm and Singular Value Decomposition. IEEE Trans. Pattern. Anal. Mach. Intell. 23(10), 1120–1136 (2001)

    Article  Google Scholar 

  35. Lozano, M.A., Escolano, F.: A Significant Improvement of Softassign with Diffusion Kernels. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds.) SSPR&SPR 2004. LNCS, vol. 3138, pp. 76–84. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  36. Rota-Bulò, S., Torsello, A., Pelillo, M.: A Continuous-Based Approach for Partial Clique Enumeration. In: Escolano, F., Vento, M. (eds.) GbRPR. LNCS, vol. 4538, pp. 61–70. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  37. Ren, P., Wilson, R.C., Hancock, E.R.: Spectral Embedding of Feature Hypergraphs. In: SSPR/SPR 2008, pp. 308–317 (2008)

    Google Scholar 

  38. Rota-Bulò, S., Albarelli, A., Pelillo, M., Torsello, A.: A Hypergraph-based Approach to Affine Parameters Estimation. In: ICPR 2008 (2008)

    Google Scholar 

  39. Xia, S., Hancock, E.R.: 3D Object Recognition Using Hyper-Graphs and Ranked Local Invariant Features. In: SSPR/SPR 2008, pp. 117–126 (2008)

    Google Scholar 

  40. Escolano, F., Hancock, E.R., Lozano, M.A.: Polytopal Graph Complexity, Matrix Permanents, and Embedding. In: SSPR/SPR 2008, pp. 237–246 (2008)

    Google Scholar 

  41. Riesen, K., Neuhaus, M., Bunke, H.: Bipartite Graph Matching for Computing the Edit Distance of Graphs. In: Escolano, F., Vento, M. (eds.) GbRPR. LNCS, vol. 4538, pp. 1–12. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Grupo ISIS, Dpto. Tecnología Electrónica, Universidad de Málaga, Spain

    R. Marfil & A. Bandera

  2. Robot Vision Group, Dpto. de Ciencia de la Computación e Inteligencia Artificial, Universidad de Alicante, Spain

    F. Escolano

Authors
  1. R. Marfil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Escolano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Bandera
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación., , , ,, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

  3. Department of Computer Architecture and Computer Technology, University of Granada, Spain

    Alberto Prieto

  4. Department of Informatics, University of Salamanca, Salamanca, Spain

    Juan M. Corchado

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Marfil, R., Escolano, F., Bandera, A. (2009). Graph-Based Representations in Pattern Recognition and Computational Intelligence. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_50

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-02478-8_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02477-1

  • Online ISBN: 978-3-642-02478-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation