Baykal, Ibrahim Cem2025-01-062025-01-062018978-1-5386-6878-8https://hdl.handle.net/20.500.14669/2598International Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEYThis article aims to design the most efficient texture discriminator by proposing two alterations to the existing algorithms. The first argument presented in this article is that the direct use of Unser's difference histogram (not sum) as input to a classifier yields more successful results than Haralick's method. The second proposal is the introduction of two different methods for the selection of the pixel distance and the orientation. Both mathematical and empirical evidence is presented, proving that these methods improve the texture discrimination rate by %250-%300 while reducing the computational complexity by %50.eninfo:eu-repo/semantics/closedAccessImage processinglinear discriminant analysisneural networkspattern recognitiontexture feature extractionConference ObjectWOS:000458717400171N/A