Texture classification using a new version of local binary patterns
Farshad Tajeripour, M. Pakdel · 2012
Among various feature extraction methods for texture classification, Local Binary Patterns and Modified Local Binary Patterns, because of simplicity and classification accuracy, have emerged as one of the most popular ones. LBP has simple implementation, but with increasing the radius of neighborhood, computational complexity will be increased. MLBP cannot classify non uniform textures as well as uniform ones. In this paper a new version of LBP is developed that has less computational complexity than LBP and more classification accuracy than MLBP. The proposed method classifies non uniform textures as well as uniform ones. Classification accuracy on two standard datasets, Brodatz and Outex, indicates efficiency of the proposed approach.