Texture Classification Using High-Order Local Derivative Pattern and KNN Classifier

References

[1] R. M. Haralick, K. Shanmugam, and I.Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610–621, 1973.

[2] T. Randen and J. H. Husøy, “Filtering for texture classification: A comparative study,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 4, pp. 291–310, 1999.

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

[4] T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996.

[5] 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, 2002.

[6] B. Zhang, Y. Gao, S. Zhao, and J. Liu, “Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor,” IEEE Transactions on Image Processing, vol. 19, no. 2, pp. 533–544, 2010.

[7] J. G. Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,” Journal of the Optical Society of America A, vol. 2, no. 7, pp. 1160–1169, 1985.

[8] 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, 2010.

[9] M. Tuceryan and A. K. Jain, “Texture analysis,” in The Handbook of Pattern Recognition and Computer Vision, 2nd ed., World Scientific, 1998, pp. 207–248.

[10] A. Materka and M. Strzelecki, “Texture analysis methods – A review,” Technical University of Lodz, Institute of Electronics, COST B11 report, Brussels, 1998.

[11] S. Arivazhagan, L. Ganesan, and S. Priyal, “Texture classification using wavelet transform,” Pattern Recognition Letters, vol. 24, no. 9–10, pp. 1513–1521, 2003.

[12] Y. Guo, G. Zhao, and M. Pietikäinen, “Discriminative features for texture description,” Pattern Recognition, vol. 45, no. 10, pp. 3834–3843, 2012.

[13] J. Zhang, Y. Yan, and M. Lades, “Face recognition: Eigenface, elastic matching, and neural nets,” Proceedings of the IEEE, vol. 85, no. 9, pp. 1423–1435, 1997.

[14] L. Nanni, S. Brahnam, and A. Lumini, “A local approach based on a Local Binary Patterns variant texture descriptor for classifying pain states,” Expert Systems with Applications, vol. 37, no. 12, pp. 7888–7894, 2010.

[15] K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed., Academic Press, 1990.

[16] T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21–27, 1967.

[17] G. Zhao and M. Pietikäinen, “Dynamic texture recognition using local binary patterns with an application to facial expressions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 915–928, 2007.

[18] S. Liao, M. W. K. Law, and A. C. S. Chung, “Dominant local binary patterns for texture classification,” IEEE Transactions on Image Processing, vol. 18, no. 5, pp. 1107–1118, 2009.

@article{1714818,
      author = {Bandaru Satish Babu, Muthyala Bhavana, Malempati Hema SriVani, Rubeena Mehak, Yarram Seeta Rama Karthik},
      title = {Texture Classification Using High-Order Local Derivative Pattern and KNN Classifier},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {291-297},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1714818.pdf},
      abstract = {Texture classification is a fundamental issue in image processing and computer vision. It has been used in material classification, surface analysis, document analysis, and industrial automation. In this paper, a texture classification algorithm based on Local Derivative Pattern (LDP) is proposed. The algorithm extracts high-order directional texture features from grayscale images and represents them using normalized histograms. A K-Nearest Neighbor (KNN) classifier with cosine distance is employed to classify texture images into multiple categories. Simulation experiments on a practical texture image database demonstrate that the proposed algorithm can achieve accurate classification results with low computational complexity.},
      keywords = {Local Derivative Pattern, Texture Classification, High-Order Descriptor, Cosine Distance, KNN, Image Texture Analysis.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1714818}
  }