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Journals / 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
Pages
338–341
Publication Date
2016-12-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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 Key Takeaways

🎯
Objective & Hypothesis:In many texture recognition problems, Local Binary Patterns (LBP) method is used for feature extraction.
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Key Findings & Data:This method is based on comparison of each centre pixel and its neighbours’ intensity value in image.
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Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables: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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Cite This Article

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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References (8)

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