Advertisement

Texture Discrimination Using Hierarchical Complex Networks

  • Thomas Chalumeau
  • Luciano da F. Costa
  • Olivier Laligant
  • Fabrice Meriaudeau
Part of the Multimedia Systems and Applications Series book series (MMSA, volume 31)

Summary

Texture analysis represents one of the main areas in image processing and computer vision. The current article describes how complex networks have been used in order to represent and characterized textures. More speci?cally, networks are derived from the texture images by expressing pixels as network nodes and similarities between pixels as network edges. Then, measurements such as the node degree, strengths and clustering coe?cient are used in order to quantify properties of the connectivity and topology of the analyzed networks. Because such properties are directly related to the structure of the respective texture images, they can be used as features for characterizing and classifying textures. The latter possibility is illustrated with respect to images of textures, DNA chaos game, and faces. The possibility of using the network representations as a subsidy for DNA characterization is also discussed in this work.

Keywords

Complex Network Hierarchical Level Node Degree Texture Image Canonical Variable Analysis 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Mark E. J. Newman, “The structure and function of complex networks,” condmat/ 0303516, 2003.Google Scholar
  2. 2.
    Luciano da Fontoura Costa, Francisco A. Rodrigues, Gonzalo Travieso, and P. R. Villas Boas, “Characterization of complex networks: A survey of measurements,” cond-mat/0505185, 2005.Google Scholar
  3. 3.
    Reka Albert and Albert-Laszlo Barabasi, “Statistical mechanics of complex networks,” cond-mat/0106096, 2001.Google Scholar
  4. 4.
    Luciano da Fontoura Costa, “Complex networks, simple vision,” condmat/ 0403346, 2004.Google Scholar
  5. 5.
    Luciano da Fontoura Costa, “Hub-based community finding,” condmat/ 0405022, 2004.Google Scholar
  6. 6.
    Luciano da Fontoura Costa, “Hierarchical characterization of complex networks,” cond-mat/0412761, 2005.Google Scholar
  7. 7.
    T. Chalumeau, L. da F. Costa, O. Laligant, and F. Meriaudeau, “Optimized texture classification by using hierarchical complex networks measurements,” Machine Vision Applications in Industrial Inspection XIV, vol. 6070, 2006.Google Scholar
  8. 8.
    J. S. Almeida, J. A. Carriçco, A. Maretzek, P. A. Noble, and M Fletcher, “Analysis of genomic sequences by chaos game representation,” BIOINFORMATICS, vol. 17, pp. 429–437, 2001.Google Scholar
  9. 9.
    Reinhard Diestel, Graph Theory, Springer, 2nd electronic edition edition, 2000.Google Scholar
  10. 10.
    H. Joel Jeffrey, “Chaos game representation of gene structure,” Nucleic Acids Research, vol. 18 (8), pp. 2163–2170, 1990.CrossRefGoogle Scholar
  11. 11.
    R. O. Duda, P. E. Hart, , and D. G. Stork, “Pattern classification,” Wiley Interscience, 2001.Google Scholar
  12. 12.
    Luciano da Fontoura Costa and Roberto M. Cesar Junior, Shape Analysis and Classification: Theory and Practice, CRC Press, Inc, 2000.Google Scholar
  13. 13.
    Ricco Rakotomalala, “Tanagra : un logiciel gratuit pour l’enseignement et la recherche,” in Actes de EGC’2005, RNTI-E-3, vol. 2, pp. 697–702, 2005.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Thomas Chalumeau
    • 1
  • Luciano da F. Costa
    • 2
  • Olivier Laligant
    • 1
  • Fabrice Meriaudeau
    • 1
  1. 1.Universite de Bourgogne - Le2iFrance
  2. 2.Universidade de Sao Paulo- IFSCSao CarlosBrasil

Personalised recommendations