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An effective scheme for image texture classification based on binary local structure pattern

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Abstract

Effectiveness of local binary pattern (LBP) features is well proven in the field of texture image classification and retrieval. This paper presents a more effective completed modeling of the LBP. The traditional LBP has a shortcoming that sometimes it may represent different structural patterns with same LBP code. In addition, LBP also lacks global information and is sensitive to noise. In this paper, the binary patterns generated using threshold as a summation of center pixel value and average local differences are proposed. The proposed local structure patterns (LSP) can more accurately classify different textural structures as they utilize both local and global information. The LSP can be combined with a simple LBP and center pixel pattern to give a completed local structure pattern (CLSP) to achieve higher classification accuracy. In order to make CLSP insensitive to noise, a robust local structure pattern (RLSP) is also proposed. The proposed scheme is tested over three representative texture databases viz. Outex, Curet, and UIUC. The experimental results indicate that the proposed method can achieve higher classification accuracy while being more robust to noise.

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Acknowledgements

The authors sincerely thank MVG, Zhao, and Guo for sharing the source codes of LBP, CRLBP, and CLBP.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Raghogarh, Guna, MP, 473226, India

    Nishant Shrivastava & Vipin Tyagi

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  1. Nishant Shrivastava
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  2. Vipin Tyagi
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Correspondence to Vipin Tyagi.

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Shrivastava, N., Tyagi, V. An effective scheme for image texture classification based on binary local structure pattern. Vis Comput 30, 1223–1232 (2014). https://doi.org/10.1007/s00371-013-0887-0

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