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Local binary patterns versus signal processing texture analysis: a study from a performance evaluation perspective

Local binary patterns versus signal processing texture analysis: a study from a performance... Purpose – The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro‐level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters. Design/methodology/approach – The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases. Findings – The most important finding resulting from this study is that although the LBP/C and the multi‐channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture. Practical implications – Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding. Originality/value – This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi‐channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensor Review Emerald Publishing

Local binary patterns versus signal processing texture analysis: a study from a performance evaluation perspective

Sensor Review , Volume 32 (2): 14 – Mar 23, 2012

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Publisher
Emerald Publishing
Copyright
Copyright © 2012 Emerald Group Publishing Limited. All rights reserved.
ISSN
0260-2288
DOI
10.1108/02602281211209446
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro‐level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters. Design/methodology/approach – The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases. Findings – The most important finding resulting from this study is that although the LBP/C and the multi‐channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture. Practical implications – Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding. Originality/value – This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi‐channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases.

Journal

Sensor ReviewEmerald Publishing

Published: Mar 23, 2012

Keywords: Signal processing; Pattern recognition; Texture classification; Local binary patterns; Rotational invariance; Gabor filters; Isotropic filters

References