Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition

Year 2016, , 338 - 341, 01.12.2016

Nihan Kazak

https://doi.org/10.18100/ijamec.270683

Cited By: 8

Abstract

In many texture recognition problems, Local Binary
Patterns (LBP) method is used for feature extraction. This method is based on
comparison of each centre pixel and its neighbours’ intensity value in image.
Due to its simplicity of calculation, LBP has become one of the most popular
feature extraction techniques. In literature, different neighbourhood
topologies of LBP structure are given such as circle, square, ellipse,
parabola, hyperbola, and Archimedean spiral. This paper focuses on the use of
uniform and basic LBP that have spiral topology in texture classification. We
first derive basic and uniform LBP features based on spiral topology. Then the
performances of several classification methods such as linear discriminant analysis
(LDA), linear regression classifier (LRC), support vector machines (SVM),
Chi-square test, and G-test are compared using these features in UIUC texture
database.

Keywords

Local binary patterns, texture recognition, feature extraction, classification methods, spiral topology

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