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

T. Chalumeau; O. Laligant; L. da F. Costa; F. Meriaudeau

doi:10.1117/12.691782

12 October 2006 Complex network representation of textures: new perspectives for texture characterization and classification

T. Chalumeau, O. Laligant, L. da F. Costa, F. Meriaudeau

Author Affiliations +

Proceedings Volume 6383, Wavelet Applications in Industrial Processing IV; 63830Q (2006) https://doi.org/10.1117/12.691782
Event: Optics East 2006, 2006, Boston, Massachusetts, United States

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.

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T. Chalumeau, O. Laligant, L. da F. Costa, and F. Meriaudeau "Complex network representation of textures: new perspectives for texture characterization and classification", Proc. SPIE 6383, Wavelet Applications in Industrial Processing IV, 63830Q (12 October 2006); https://doi.org/10.1117/12.691782

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