Feature based local binary pattern for rotation invariant texture classification

Abstract The local binary pattern (LBP) descriptor is widely used in texture analysis because of its computational simplicity and robustness to illumination changes. However, LBP has limitations to fully capture discriminative information since only the sign information of the difference vector in a local region is used. To enhance the performance of LBP, we propose a new descriptor for texture classification—feature based local binary pattern (FbLBP). In the proposed FbLBP, difference vector is decomposed into sign part and magnitude part, the sign part is described by conventional LBP, while the magnitude part is described by two features of the mean and the variance of the magnitude vector. The way we extract magnitude information in difference vector shows high complementarity to the sign part and less sensitive to illumination changes with a low dimensionality. Furthermore, an adaptive local threshold is used to convert these two features into binary codes. The proposed low dimensional FbLBP is very fast to construct and no parameters are required to tune for different kinds of databases. Experimental results on four representative texture databases of Outex, CUReT, UIUC, and XU_HR show that the proposed FbLBP achieves more than 10% improvement compared with conventional LBP and 1%–3% improvement compared with the best classification accuracy among other benchmarked state-of-the-art LBP variants.