Multi-Scale Cross-Band Encoding of Sectored Local Binary Pattern for Robust Texture Classification

摘要

The original Local Binary Pattern (LBP) has limited discriminative power and is sensitive to noise. In view of this, this paper proposes a novel image descriptor called Multi-Scale Cross-Band Encoding of Sectored Local Binary Pattern (MCE-SLBP) for robust texture classification. First, the pyramid decomposition is explored to obtain multi-scale low-frequency and high-frequency (difference) images. To encode more discriminative features, these high-frequency images are further decomposed into positive and negative high-frequency images via the polarity splitting. Then, a robust Sectored Local Binary Pattern (SLBP) is proposed to compute texture feature codes on the decomposed images via cross-band joint coding. Finally, a multi-scale histogram representation is obtained by concatenating histograms of texture codes computed at all decomposition levels. Experiments on three benchmark texture databases (i.e., Outex, Brodatz and CUReT) demonstrate that the proposed method achieves the state-of-the-art classification accuracies both under noise-free conditions and in the presence of different levels of Gaussian noise.