Research Article Open Access

An Innovative Technique of Texture Classification and Comparison Based on Long Linear Patterns

V. Vijaya Kumar1, B. Eswara Reddy1, U. S.N. Raju2 and K. Chandra Sekharan1
  • 1 , Afganistan
  • 2 ,
Journal of Computer Science
Volume 3 No. 8, 2007, 633-638

DOI: https://doi.org/10.3844/jcssp.2007.633.638

Submitted On: 5 September 2007 Published On: 28 August 2007

How to Cite: Kumar, V. V., Reddy, B. E., Raju, U. S. & Sekharan, K. C. (2007). An Innovative Technique of Texture Classification and Comparison Based on Long Linear Patterns . Journal of Computer Science, 3(8), 633-638. https://doi.org/10.3844/jcssp.2007.633.638

Abstract

The present paper proposes a method of texture classification based on long linear patterns. Linear patterns of long size are bright features defined by morphological properties: linearity, connectivity, width and by a specific Gaussian-like profile whose curvature varies smoothly along the crest line. The most significant information of a texture often appears in the occurrence of grain components. That’s why the present paper used sum of occurrence of grain components for feature extraction. The features are constructed from the different combination of long linear patterns with different orientations. These features offer a better discriminating strategy for texture classification. Further, the distance function captured from the sum of occurrence of grain components of textures is expected to enhance the class seperability power. The class seperability power of these features is investigated in the classification experiments with arbitrarily chosen texture images taken from the Brodatz album. The experimental results indicated good analysis, and how the classification of textures will be effected with different long linear patterns.

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Keywords

  • Orientations
  • Linearity
  • Connectivity
  • Features