Journal Article Texture Classification Based on Complex Network Model with Spatial Information

Thewsuwan, Srisupang  ,  Horio, Keiichi

This paper proposes a method for image texture classification based on a complex network model. Finding relevant and valuable information in an image texture is an essential issue for image classification and remains a challenge. Recently, a complex network model has been used for texture analysis and classification. However, with current analysis methods, important empirical properties of image texture such as spatial information are discarded from consideration. Accordingly, we propose local spatial pattern mapping (LSPM) method for manipulating the spatial information in an image texture with multi-radial distance analysis to capture the texture pattern. In experiments, the feature properties under the traditional complex network model and those with the proposed method are analyzed by using the Brodatz, UIUC, and Outex databases. As results, the proposed method is shown to be effective for texture classification, providing an improved classification rate as compared to the traditional complex network model.

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