Efficient fractal method for texture classification

Abstract

This paper presents an alternative approach to classical box counting algorithm for fractal dimension estimation. Irrelevant data are eliminated from input sequences of the algorithm and a new fractal dimension, called efficient fractal dimension (EFD), which is based on the remaining sequences is calculated. The discriminating capacity and the time efficiency of EFD are evaluated in comparison with fractal dimension (FD) computed by box counting both theoretically and empirically. The results revealed that EFD is better than FD for texture identification and classification.