Rotation invariant texture classification using adaptive LBP with directional statistical features

Zhenhua Guo; Lei Zhang; Dapeng Zhang; Su Zhang

doi:10.1109/ICIP.2010.5652209

Rotation invariant texture classification using adaptive LBP with directional statistical features

Zhenhua Guo, Lei Zhang, Dapeng Zhang, Su Zhang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

83 Citations (Scopus)

Abstract

Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. Traditional LBP codes the sign of the local difference and uses the histogram of the binary code to model the given image. However, the directional statistical information is ignored in LBP. In this paper, some directional statistical features, specifically the mean and standard deviation of the local absolute difference are extracted and used to improve the LBP classification efficiency. In addition, the least square estimation is used to adaptively minimize the local difference for more stable directional statistical features, and we call this scheme the adaptive LBP (ALBP). By coupling the directional statistical features with ALBP, a new rotation invariant texture classification method is presented. Experiments on a large texture database show that the proposed texture feature extraction and classification scheme could significantly improve the classification accuracy of LBP.

Original language	English
Title of host publication	2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages	285-288
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2010.5652209
Publication status	Published - 1 Dec 2010
Event	2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong Duration: 26 Sept 2010 → 29 Sept 2010

Conference

Conference	2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/Territory	Hong Kong
City	Hong Kong
Period	26/09/10 → 29/09/10

Keywords

LBP
LSE
Rotation invariance

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

More information

10.1109/ICIP.2010.5652209

Cite this

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title = "Rotation invariant texture classification using adaptive LBP with directional statistical features",

abstract = "Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. Traditional LBP codes the sign of the local difference and uses the histogram of the binary code to model the given image. However, the directional statistical information is ignored in LBP. In this paper, some directional statistical features, specifically the mean and standard deviation of the local absolute difference are extracted and used to improve the LBP classification efficiency. In addition, the least square estimation is used to adaptively minimize the local difference for more stable directional statistical features, and we call this scheme the adaptive LBP (ALBP). By coupling the directional statistical features with ALBP, a new rotation invariant texture classification method is presented. Experiments on a large texture database show that the proposed texture feature extraction and classification scheme could significantly improve the classification accuracy of LBP.",

keywords = "LBP, LSE, Rotation invariance",

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doi = "10.1109/ICIP.2010.5652209",

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Guo, Z, Zhang, L, Zhang, D & Zhang, S 2010, Rotation invariant texture classification using adaptive LBP with directional statistical features. in 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings., 5652209, pp. 285-288, 2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, Hong Kong, 26/09/10. https://doi.org/10.1109/ICIP.2010.5652209

Rotation invariant texture classification using adaptive LBP with directional statistical features. / Guo, Zhenhua; Zhang, Lei; Zhang, Dapeng et al.
2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings. 2010. p. 285-288 5652209.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Rotation invariant texture classification using adaptive LBP with directional statistical features

AU - Guo, Zhenhua

AU - Zhang, Lei

AU - Zhang, Dapeng

AU - Zhang, Su

PY - 2010/12/1

Y1 - 2010/12/1

N2 - Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. Traditional LBP codes the sign of the local difference and uses the histogram of the binary code to model the given image. However, the directional statistical information is ignored in LBP. In this paper, some directional statistical features, specifically the mean and standard deviation of the local absolute difference are extracted and used to improve the LBP classification efficiency. In addition, the least square estimation is used to adaptively minimize the local difference for more stable directional statistical features, and we call this scheme the adaptive LBP (ALBP). By coupling the directional statistical features with ALBP, a new rotation invariant texture classification method is presented. Experiments on a large texture database show that the proposed texture feature extraction and classification scheme could significantly improve the classification accuracy of LBP.

AB - Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. Traditional LBP codes the sign of the local difference and uses the histogram of the binary code to model the given image. However, the directional statistical information is ignored in LBP. In this paper, some directional statistical features, specifically the mean and standard deviation of the local absolute difference are extracted and used to improve the LBP classification efficiency. In addition, the least square estimation is used to adaptively minimize the local difference for more stable directional statistical features, and we call this scheme the adaptive LBP (ALBP). By coupling the directional statistical features with ALBP, a new rotation invariant texture classification method is presented. Experiments on a large texture database show that the proposed texture feature extraction and classification scheme could significantly improve the classification accuracy of LBP.

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BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings

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Rotation invariant texture classification using adaptive LBP with directional statistical features

Abstract

Conference

Keywords

ASJC Scopus subject areas

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