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

@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}
  }