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A MAD-based Robust Fractal Dimension Calculating Method and Its Application in Image Recognition

WU Hai-yan, ZHANG Chen-chen   

  1. College of Mathematics and Computational Science, Shenzhen University, Shenzhen 518060, China
  • Received:2013-04-17 Revised:1900-01-01 Online:2013-12-18 Published:2013-12-18

Abstract: The traditional robust differential box-counting method (RDBC) has been successfully used for calculating fractal dimension of an image degraded by Gaussian noise. However, it is not suitable for estimating fractal dimension of salt & pepper noisy images and classifying those images. This paper presents a MAD-based method (MAD-DBC) for calculating fractal dimension of an image. The method uses MAD for differential box-counting, which is robust against salt & pepper noises. Classification experiments on Brodatz texture images show that, compared with DBC and RDBC, the MAD-DBC achieves higher classification rate and better noise robustness.

Key words: fractal dimension, differential box-counting, MAD, image classification