Local binary patterns partitioning for rotation invariant texture classification

Local binary pattern (LBP) is a well-defined operator and it has been widely used in texture description. By representing a local region with its center pixel and local difference vector, LBP just encodes the sign component of this difference vector. This paper presents an operator, which efficiently encodes the magnitude part of local difference, as a complementary to LBP. We combine the sign and magnitude component of image local difference vectors, by making the joint distribution of LBP and presented magnitude based features. It has been experimentally demonstrated that, considerable improvement can be made for rotation invariant texture classification, in comparison with recently proposed completed LBP (CLBP) method.