LDA Based Discriminant Features for Texture Classification Using WT and PDE Approach

  • Rohini A. Bhusnurmath
  • P. S. Hiremath
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 801)


Texture classification is an important aspect of image processing and computer vision applications. In this paper, discriminant features for texture classification using WT (wavelet transform) and PDE (partial differential equation) approach is presented. WT is used to obtain directional information of image. A PDE for anisotropic diffusion is used to extract texture component. Different statistical features are computed from texture component. The linear discriminant analysis (LDA) is used to boost the class separability. The discriminant features so obtained are robust class representatives. The robustness of proposed approach is experimented using three gray scale texture datasets: Oulu, Kylberg and VisTex. The k-NN classifier is used to evaluate the classification accuracy. The experimental results exhibit better performance as compared to the other methods in the literature in terms of computational efficiency.


Discriminant texture features Texture classification PDE k-NN Image processing Wavelet transform 



The authors are grateful to the reviewers for their valuable comments and suggestions, which substantially improved the quality of the paper.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceGovernment P.U. College for GirlsVijayapurIndia
  2. 2.Department of Computer Science (MCA)KLE Technological UniversityHubliIndia

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