Abstract
In this paper, we propose a novel texture analysis method using the complex network theory. It was investigated how a texture image can be effectively represented, characterized and analyzed in terms of a complex network. The propose uses degree measurements in a dynamic evolution network to compose a set of feasible shape descriptors. Results show that the method is very robust and it presents a very excellent texture discrimination for all considered classes. © 2010 Springer-Verlag.
Cite
CITATION STYLE
Backes, A. R., Casanova, D., & Bruno, O. M. (2010). A complex network-based approach for texture analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6419 LNCS, pp. 354–361). https://doi.org/10.1007/978-3-642-16687-7_48
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