Journal of System Simulation ›› 2015, Vol. 27 ›› Issue (9): 1951-1959.

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Texture Classification Based on Multi-scale Wavelet

Liao Ning1, Xu Lisha1, Qian Xiaoshan2   

  1. 1. Shewai School, Central South University of forestry and Technology, Changsha 410004, China;
    2. Physical Science and Technology College, Yichun University, Yichun 336000, China
  • Received:2015-04-18 Revised:2015-07-22 Online:2015-09-08 Published:2020-08-07
  • About author:Liao ning(1982-), female, Hunan, Graduate, Lecturer, Research direction: Intelligent information processing; Xu Lisha(1982-), female, Hunan, Graduate, Lecturer, Research direction: Intelligent information processing.
  • Supported by:
    The National Natural Science Foundation of China (0634020, 60874069, 60804037); 863 projects (2006AA04Z181); Yichun University research projects (XJ1314)

Abstract: Texture analysis is quite sensitive to rotations. An efficient approach, called Invariant Contourlet-Fourier Descriptor, was proposed to achieve rotation invariance in texture analysis by extracting a set of Shannon entropy in contourlet domain. Discrete Fourier Transform analysis was applied to entropy vectors of each scale to form rotation invariant feature vectors, the dimensionality of which was reduced further due to the symmetry of DFT magnitude spectrum. Two classifiers, including the well-known Euclidean distance and Support Vector Machine, were studied to measure the distance between the known and unknown features. Experimental results on 1500 texture images show that contourlet is an efficient tool to represent directional texture, and the rotation invariant texture features were effective to achieve accurate classification with low computational complexity.

Key words: multi-scale wavelet, discrete fourier transform, shannon entropy, support vector machine, texture classification.

CLC Number: