BRINT: A binary rotation invariant and noise tolerant texture descriptor
Li Liu, Bing Yang, Paul Fieguth, Zheng Yang, Yingmei Wei · 2013
Local Binary Pattern (LBP) and its variants are effective and popular descriptors for texture classification. Most LBP like descriptors have disadvantages including sensitiveness to noise and inability to capture long distance texture information. In this paper we propose a simple, efficient, yet robust multi-resolution descriptor to texture classification - Binary Rotation Invariant and Noise Tolerant (BRINT). The proposed descriptor is very fast to build, very compact while remaining robust to illumination variations, rotation changes and noise. We develop a novel and simple strategy - averaging before binarization - to compute a local binary descriptor based on the conventional LBP approach. Points are sampled in a circular neighborhood, but keeping the number of bins in a single-scale LBP histogram constant and small by averaging over several contiguous pixels in the circle. There is no need for pre-training, no texton dictionary, and no tuning of parameters to deal with different datasets. Experiments on the Outex test suite demonstrate that the proposed approach is very robust to noise and significantly outperforms the state-of-the-art in terms of classifying noise corrupted textures.