Machine Learning–Based Texture Classification Using Sorted Consecutive Local Binary Pattern Features

References

[1] T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, Jul. 2002.

[2] Z. Guo, L. Zhang, and D. Zhang, “A completed modeling of local binary pattern operator for texture classification,” IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1657–1663, Jun. 2010.

[3] X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1635–1650, Jun. 2010.

[4] S. Ryu, B. Oh, and K. Sohn, “Sorted consecutive local binary pattern for texture classification,” Journal of Pattern Recognition Research, vol. 10, no. 2, pp. 105–120, 2015.

[5] R. M. Haralick, “Statistical and structural approaches to texture,” Proceedings of the IEEE, vol. 67, no. 5, pp. 786–804, May 1979.

[6] D. Zhang and G. Lu, “Review of shape representation and description techniques,” Pattern Recognition, vol. 37, no. 1, pp. 1–19, Jan. 2004.

[7] L. Liu, P. Fieguth, D. Clausi, and G. Kuang, “Sorted random projections for robust texture classification,” Pattern Recognition, vol. 48, no. 3, pp. 1085–1098, Mar. 2015.

[8] J. Yang, Y. Yu, and S. Zhu, “Local binary pattern based texture classification using machine learning methods,” Pattern Recognition Letters, vol. 34, no. 10, pp. 1155–1161, Jul. 2013.

[9] C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.

[10] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. Upper Saddle River, NJ, USA: Pearson Education, 2008.

[11] M. Pietikäinen, A. Hadid, G. Zhao, and T. Ahonen, Computer Vision Using Local Binary Patterns. London, U.K.: Springer, 2011.

[12] L. Nanni, S. Brahnam, and A. Lumini, “A survey on LBP based texture descriptors,” Expert Systems with Applications, vol. 39, no. 3, pp. 3634–3641, Feb. 2012.

[13] T. Ahonen, A. Hadid, and M. Pietikäinen, “Face recognition with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037–2041, Dec. 2006.

[14] V. Ojansivu and J. Heikkilä, “Blur insensitive texture classification using local phase quantization,” in Proc. International Conference on Image and Signal Processing (ICISP), Cherbourg-Octeville, France, 2008, pp. 236–243.

[15] L. Liu, L. Zhao, Y. Long, G. Kuang, and P. Fieguth, “Extended local binary patterns for texture classification,” Image and Vision Computing, vol. 30, no. 2, pp. 86–99, Feb. 2012.

@article{1715162,
      author = {Dr. A. SriKrishna, S. Divija Lakshmi, M. Chetanya Lahari, S K. Sameer, T. Prasanna Lakshmi},
      title = {Machine Learning–Based Texture Classification Using Sorted Consecutive Local Binary Pattern Features},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {1280-1285},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715162.pdf},
      abstract = {Texture classification is a fundamental task in computer vision and image analysis, widely used in applications such as medical imaging, material inspection, remote sensing, and object recognition. Among various texture descriptors, Local Binary Pattern (LBP) has gained significant attention due to its simplicity and computational efficiency. However, conventional LBP methods often ignore certain binary patterns with multiple transitions, which may lead to loss of important texture information. To overcome this limitation, this paper presents a machine learning based texture classification approach based on Sorted Consecutive Local Binary Pattern (scLBP). The proposed method extracts local texture features by analyzing binary relationships between neighboring pixels and sorting consecutive patterns to preserve detailed structural information. The extracted scLBP feature vectors are then used as input to a machine learning classifier for texture recognition. Experimental analysis shows that the proposed approach provides improved feature representation and achieves better classification performance compared with traditional LBP-based techniques. The results demonstrate that the scLBP-based framework can effectively enhance texture classification accuracy and robustness.},
      keywords = {Texture Classification, Local Binary Pattern (LBP), Sorted Consecutive Local Binary Pattern (scLBP), Machine Learning, Image Processing.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715162}
  }