Incorporating texture intensity information into LBP-based operators

Abstract

In this paper, we aim to improve the accuracy of LBP-based operators by including texture image intensity characteristics in the operator. We utilize shifted step function to minimize the quantization error of the step function to obtain more discriminative operators. Features obtained from shifted step function are simply fused together to form the final histogram. This model is generalized and can be integrated with other existing LBP variants to reduce quantization error of the step function for texture classification. The proposed method is integrated with multiple LBP-based feature descriptors and evaluated on publicly available texture databases (Outex-TC-00012 and KTH-TIPS2b) for texture classification. Experimental results demonstrate that it not only improves the performance of operators it is integrated with but also achieves higher accuracy compared to the state of the art in texture classification. © 2013 Springer-Verlag.