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Proceedings Paper

Complex network representation of textures: new perspectives for texture characterization and classification
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Paper Abstract

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 specifically, 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 coefficient 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.

Paper Details

Date Published: 12 October 2006
PDF: 6 pages
Proc. SPIE 6383, Wavelet Applications in Industrial Processing IV, 63830Q (12 October 2006); doi: 10.1117/12.691782
Show Author Affiliations
T. Chalumeau, Univ. de Bourgogne (France)
Univ. de Sao Paulo (Brazil)
O. Laligant, Univ. de Bourgogne (France)
L. da F. Costa, Univ. de Sao Paulo (Brazil)
F. Meriaudeau, Univ. de Bourgogne (France)


Published in SPIE Proceedings Vol. 6383:
Wavelet Applications in Industrial Processing IV
Frédéric Truchetet; Olivier Laligant, Editor(s)

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