Texture Characterization via Automatic Threshold Selection on Image-Generated Complex Network

This work presents an automated approach to texture characterization through complex networks. By applying an automatic threshold selection for network degree map generation, we managed to achieve significant reduction in the number of descriptors used. The method is adaptive to any image database, because it is based on the analysis of the energy value of the degree histogram of the complex networks generated particularly from each database. Experiments using the proposed method for texture classification using databases from literature show that the proposed method can not only reduce feature vector size, but in some cases also improve correct classification rates when compared to other state of the art methods.