A Novel Texture Classification Scheme based on Completed Multiple Adaptive Threshold Patterns
Nishant Shrivastava · Procedia Computer Science · 2020
Local binary patterns and their extensions are heavily used in image texture classification. However traditional LBP codes are sensitive to noise and have limited capability of discriminating various texture patterns. This study presents a novel Completed multiple adaptive threshold patterns (CMATP) descriptor for texture classification to ensure higher classification rates. For each patch of pixels statistical parameters like mean, standard deviation and value of centre pixel are computed to determine structural properties. Based on these observations pixel patches are categorized in to uniform, moderate and non-uniform structures. A suitable adaptive threshold is then obtained for each class of pixel groups, which can generate binary pattern that are more uniform and can produce distinct binary code for different texture patterns. Further to extract centre pixel and contrast information from each patch, multiple threshold based complementary sign and magnitude patterns are generated in a similar way. The sign and magnitude pattern are then combined form CMATP descriptor. We have compared the proposed CMATP pattern with some state of the art LBP based descriptors on some benchmark datasets like Outex, Brodatz and UMD. The experimental results shows superior performance of the CMATP as compared to others.