Noise tolerant local binary pattern operator for efficient texture analysis

The local binary pattern (LBP) operator is a very effective multi-resolution texture descriptor that can be applied in many image processing applications. However, existing LBP operators can not use the information of non-uniform patterns efficiently and they are also sensitive to noise. This paper proposes a noise tolerant extension of LBP operators to extract statistical and structural image features for efficient texture analysis. The proposed LBP operator uses a circular majority voting filter and suitable rotation-invariant labeling scheme to obtain more regular uniform and non-uniform patterns that have better discrimination ability and more robustness against noise. Experimental results on the Brodatz, CUReT and MeasTex databases show the improvement of the proposed LBP operator performance, especially when a large number of neighbors are used for extracting texture patterns.

► A noise tolerant extension of LBP operators is established.
► A circular majority vote filter is used to reduce the effect of noise.
► More regular uniform and non-uniform patterns are obtained using a new labeling scheme.
► The performance of the proposed LBP operator is higher than other LBP operators.