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Volume 26 Issue 3
Mar.  2004
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Hu Jin-yan, Zhang Tai-yi, Zhang Chun-mei. Texture Classification Using Fractional Brownian Motion and Probabilistic Neural Network[J]. Journal of Electronics & Information Technology, 2004, 26(3): 389-393.
Citation: Hu Jin-yan, Zhang Tai-yi, Zhang Chun-mei. Texture Classification Using Fractional Brownian Motion and Probabilistic Neural Network[J]. Journal of Electronics & Information Technology, 2004, 26(3): 389-393.

Texture Classification Using Fractional Brownian Motion and Probabilistic Neural Network

  • Received Date: 2002-11-23
  • Rev Recd Date: 2003-04-07
  • Publish Date: 2004-03-19
  • In this paper, the fractal scale is introduced as a new measure to describe natural physical surfaces. An Intercept And Linearity (IAL) method is proposed to determine the fractal scale automatically. Experiments indicate that the fractal scale, together with the fractal dimension can effectively reflect the fractal scale range of natural textures and the texture roughness within the scale range. For the purpose of classification, the fractal scales and the fractal dimensions along horizontal, vertical and diagonal directions are obtained to form a feature set with six parameters. The probabilistic neural network is used as the texture classifier. Results have shown satisfied classification of natural textures.
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  • Mandelbrot B B, Van Ness J W. Fractional Brownian motion, fractional noise and applications[J].SIAM Rev.1968, 10(4):422-437[2]Pentland A P. Fractal-based description of natural scenes. IEEE Trans. on Pattern Anal. Machine Intell., 1984, PAMI-6(11): 666-674.[3]Liu S, Chang S. Dimension estimation of discrete-time fractional Brownian motion with applications to image texture classification. IEEE Trans. on Image Processing, 1997, IP-6(8): 1176-1184.[4]Stewart C V, Moghaddam B, Hintzm K J, et al.. Fractional Brownian motion models for synthetic aperture radar imagery scene segmentation[J].Proc. IEEE.1993, 81(10):1511-1522[5]Potlapalli H, Luo R C. Fractal-based classification of natural textures[J].IEEE Trans. on Industrial Electronics.1998, 45(2):142-150[6]Chen C C, Daponte J S, Fox M D. Fractal feature analysis and classification in medical imaging.IEEE Trans. Med. Imag., 1989, MI-8(6): 133-142.[7]Chen E L, Chung P C, Chen C L, et al.. An automatic diagnostic system for CT liver image classification[J].IEEE Trans. on Biomedical Engineering.1998, 45(6):783-794[8]Wu C M, Chen Y C, Hsieh K S. Texture feature for classification of ultrasonic liver images. IEEE Trans. Med. Imag., 1992, MI-11(4): 141-152.[9]Keller J M, Chen S. Texture description and segmentation through fractal geometry[J].Computer Vision, Graphics and Image Processing.1989, 45(2):150-166[10]Kaplan L M, Kuo J C. Texture roughness analysis and synthesis via extended self-similar (ESS)model. IEEE Trans. on Pattern Anal. Machine Intell., 1995, PAMI-17(11): 1043-1056.[11]Yokoya N, Yamamoto K. Fractal-based analysis and interpolation of 3D natural surface shapes and their application to terrian modeling[J].Computer Vision, Graphics and Image Processing.1989, 46(6):284-302[12]Brodatz P. Textures: A Photographic Album for Artists and Designers, New York: Dover, 1966:9-112.
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