Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6940097 | Pattern Recognition Letters | 2018 | 9 Pages |
Abstract
In texture classification, methods using multi-resolution directional (MRD) filters such as Gabor have not often shown significantly better performance than simple methods using local binary patterns, although they have a robust theoretical background and high computational complexity. We expect that this is because such methods usually make use of only the modulus parts of complex-valued MRD-filtered images and do not fully utilize their phase parts and other directional information. This letter presents a rotation-invariant feature using four types of directional statistics obtained from both the modulus and phase parts of Gabor-filtered images. First, modulus statistics, scale-shift cross-correlations, and orientation-shift cross-correlations are computed over all directions for each pixel of Gabor-filtered images, and global autocorrelations are computed over all pixels of each Gabor-filtered image. Global means and standard deviations for the three types of directional statistics and directional means and standard deviations for the global autocorrelations are then computed to form a feature vector. Experimental results with Brodatz, STex, CUReT, KTH-TIPS, UIUC, UMD, ALOT, and Kylberg databases show that the proposed method yields excellent performance compared with several conventional methods.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Nam Chul Kim, Hyun Joo So,