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Comparative Analysis of Texture Classification Using Local Binary Pattern and Its Variants

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  • Richa Sharma

    (Punjabi University, Patiala, India)

  • Madan Lal

    (Punjabi University, Patiala, India)

Abstract

Texture classification is an important issue in digital image processing and the Local Binary pattern (LBP) is a very powerful method used for analysing textures. LBP has gained significant popularity in texture analysis world. However, LBP method is very sensitive to noise and unable to capture the macrostructure information of the image. To address its limitation, some variants of LBP have been defined. In this chapter, the texture classification performance of LBP has been compared with the five latest high-performance LBP variants, like Centre symmetric Local Binary Pattern (CS-LBP), Orthogonal Combination of Local Binary Patterns (OC LBP), Rotation Invariant Local Binary Pattern (RLBP), Dominant Rotated Local Binary Pattern (DRLBP) and Median rotated extended local binary pattern (MRELBP). This was by using the standard images Outex_TC_0010 dataset. From the experimental results it is concluded that DRLBP and MRELBP are the best methods for texture classification.

Suggested Citation

  • Richa Sharma & Madan Lal, 2017. "Comparative Analysis of Texture Classification Using Local Binary Pattern and Its Variants," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 8(2), pages 45-56, April.
    • Handle: RePEc:igg:jismd0:v:8:y:2017:i:2:p:45-56
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