期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2017
卷号：6
期号：5
页码：677-684
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Texture classification is a major issue in image analysis and pattern recognition. A number of methods are proposed in the literature including Local Binary Pattern (LBP). The LBP variant (s) plays an active role to extract texture features for texture classification. These are rotation invariant, noise sensitive or noise insensitive mehods. Each method has its own advantages and disadvantages. This paper is focused to provide a comparative analysis by evaluating the nine LBP variants using three well-known benchmark texture databases OUTEX, CUReT, UIUC using the nearest-neighbourhood classifier. The nine LBP variants are rotation invariant and uniform LBP (LBPriu2), rotation invariant LBP (LBPri), Local Ternary Pattern (LTP), Variance (VAR), LBP and VAR (LBP/VAR), Completed Local Binary Pattern (CLBP), Completed Local Binary Count (CLBC), Adjacent Evaluation Completed Local Binary Pattern (AECLBP), Adjacent Evaluation Local Ternary Pattern (AELTP). The experimental results demonstrated that, Adjacent Evaluation Completed Local Binary Pattern (AECLBP) exhibits significant improvement in classification accuracy when compared to remaining LBP variants.
关键词：Local Binary Pattern (LBP); Local Ternary Pattern (LTP); Completed Local Binary Pattern (CLBP); Completed Local Binary Count (CLBC); Adjacent Evaluation Completed Local Binary Pattern (AECLBP); texture classification; rotation invariant.
