A complex network-based approach to the analysis and classification of images

Abstract

Complex network is a topic related with a plurality of knowledge from various areas and has been applied with success in all of them. However, it is a recent area considering its application in image pattern recognition. There are few works in the literature that use the complex networks for image characterization following its analysis and classification. An image can be interpreted as a complex network wherein each pixel represents a vertex and the weighted edges are generated according to the location and intensity between two pixels. Thus, the present paper aims to investigate this type of application and explore different measurements that can be extracted from complex networks to better characterize an image. One special type of measure that we applied were those based on motifs, which are employed in several areas. However, to the best of our knowledge, motifs were never explored in complex networks representing images. The results demonstrate that our proposed methodology presented great potential, reaching up to 89.81% of accuracy for the classification of public domain image texture datasets.