This article describes a new method and approach of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we present how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditional and extended hierarchical measurements, are used to characterize "organization" of textures.
Texture characterization and classification remains an important issue in image processing and analysis. Much attention has been focused on methods involving spectral analysis and co-occurrence matrix, as well as more modern approaches such as those involving fractal dimension, entropy and criteria based in multiresolution. The present work addresses the problem of texture characterization in terms of complex networks: image pixels are represented as nodes and similarities between such pixels are mapped as links between the network nodes. It is verified that several types of textures present node degree distributions which are far distinct from those observed for random networks, suggesting complex organization of those textures. Traditional measurements of the network connectivity, including their respective hierarchical extensions, are then applied in order to obtain feature vectors from which the textures can be characterized and classified. The performance of such an approach is compared to co-occurrence methods, suggesting promising complementary perspectives.
This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditionnal and extended hierarchical measurements, are used to characterize "organisation"' of textures.
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