@article{MAKHILLIJSSCEA201912428792,
    title = {Performance Analysis of Local Binary Patterns in Texture Classification},
    journal = {International Journal of System Signal Control and Engineering Application},
    volume = {12},
    number = {4},
    pages = {74-84},
    year = {2019},
    issn = {1997-5422},
    doi = {ijssceapp.2019.74.84},
    url = {https://makhillpublications.co/view-article.php?issn=1997-5422&doi=ijssceapp.2019.74.84},
    author = {Ch. and},
    keywords = {Local Binary Patten (LBP),Local Ternary Pattern (LTP),Completed Local Binary Pattern (CLBP),Completed Local Binary Count (CLBC),AdjacentEvaluation Completed Local Binary Pattern (AECLBP),texture classification,rotation invariant},
    abstract = {This study presents a detailed comparative
study on various Local Binary Pattern (LBP) variant
texture descriptors in texture classification. The work
includes nine various texture descriptor methods namely
LBPriu2, LBPri, LTP, VAR, LBP/VAR, CLBP, CLBC,
AECLBP, AELTP. The performance of these methods is
evaluated basing on three well-known benchmark texture
databases OUTEX, CUReT, UIUC, using the nearestneighbourhood
classifier.}
    }