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)


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.


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.


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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

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