Texture classification based on image (natural and horizontal) visibility graph constructing methods

Texture classification is widely used in image analysis and some other related fields. In this paper, we designed a texture classification algorithm, named by TCIVG (Texture Classification based on Image Visibility Graph), based on a newly proposed image visibility graph network constructing method by Lacasa et al. By using TCIVG on a Brodatz texture image database, the whole procedure is illustrated. First, each texture image in the image database was transformed to an associated image natural visibility graph network and an image horizontal visibility graph network. Then, the degree distribution measure [P(k)] was extracted as a key characteristic parameter to different classifiers. Numerical experiments show that for artificial texture images, a 100% classification accuracy can be obtained by means of a quadratic discriminant based on natural TCIVG. For natural texture images, 94.80% classification accuracy can be obtained by a linear SVM (Support Vector Machine) based on horizontal TCIVG. Our results are better than that reported in some existing literature studies based on the same image database. Published under license by AIP Publishing.

Automated summary

In this study, a texture classification algorithm named TCIVG was designed based on a newly proposed image visibility graph network constructing method. Experimental results on the Brodatz texture image database showed high classification accuracy for both artificial and natural texture images. The results outperformed some existing literature studies based on the same image database.