Open AccessSome variants of spiral LBP in texture recognition
Author(s) -
Kazak Nihan,
Koc Mehmet
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.1261
Subject(s) - local binary patterns , pattern recognition (psychology) , thresholding , artificial intelligence , texture filtering , feature extraction , pixel , image texture , texture (cosmology) , mathematics , facial recognition system , computer science , computer vision , image (mathematics) , image processing , histogram
Texture classification is one of the recently popular study topics in pattern recognition. Local binary pattern (LBP) is a very efficient local texture descriptor and is used for feature extraction in texture recognition. There are five main steps in representation of texture images: neighbourhood topology and sampling, thresholding and quantisation, encoding and regrouping, combining complementary features. In this study, the authors used symmetric two spirals LBP to measure the grey‐scale difference between the centre pixel and its neighbours. They also extended the proposed method by using four spirals LBP to generate the LBP code. For classification, linear regression classification method, which is generally used to solve the face recognition problems, is used. The authors tested the performance of their method on UIUC and CUReT texture image databases. It is experimentally demonstrated that the proposed method achieves the highest classification accuracy among the comparative methods on texture databases.