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Dergiler / International Journal of Applied Mathematics Electronics and Computers / 2016 / Special Issue (2016)

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

Nihan Kazak
Sayfa
338–341
Yayın Tarihi
2016-12-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

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.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:In many texture recognition problems, Local Binary Patterns (LBP) method is used for feature extraction.
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Temel Bulgular & Veri:This method is based on comparison of each centre pixel and its neighbours’ intensity value in image.
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Sonuç & Katkı: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.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Local binary patterns · texture recognition · feature extraction

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.

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Bu Makaleye Atıf Yap

Kazak, N. (2016). Performance Analysis of Spiral Neighbourhood Topology Based Local Binary Patterns in Texture Recognition. International Journal of Applied Mathematics Electronics and Computers, Special Issue (2016), 338–341. https://ajindex.com/articles/258210

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Kaynakça (8)

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