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Texture Classification Framework Using Gabor Filters and Local Binary Patterns

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Intelligent Computing (SAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 858))

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Abstract

In this paper, a novel method towards rotation, scale and illumination invariant texture image classification is introduced. We exploit the useful rotation and scale characteristics of Gabor filters and illumination invariance characteristics of LBP, proposing image features which are invariant to the above mentioned imaging dynamics. The images are first filtered using Gabor filters followed by a summation of filter responses across scales. An LBP of the resulting features is calculated followed by an integral histogram of LBPs across various orientations of the Gabor filters. An experimental validation of the invariance of the descriptor is shown on a texture classification problem using two publicly available datasets: the USC-SIPI, and CUReT texture datasets. Our experiments show that the proposed descriptor outperforms the other methods that have been considered in this paper.

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Notes

  1. 1.

    http://www.cs.waikato.ac.nz/ml/weka/.

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Authors and Affiliations

  1. National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Farhan Riaz, Ali Hassan & Saad Rehman

Authors
  1. Farhan Riaz
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  2. Ali Hassan
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  3. Saad Rehman
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Corresponding author

Correspondence to Farhan Riaz .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Department of Information Science, Saga University, Honjo, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Riaz, F., Hassan, A., Rehman, S. (2019). Texture Classification Framework Using Gabor Filters and Local Binary Patterns. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 858. Springer, Cham. https://doi.org/10.1007/978-3-030-01174-1_43

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