A noise-robust adaptive hybrid pattern for texture classification

Publication Type:
Conference Proceeding
Citation:
Proceedings - 22nd International Conference on Pattern Recognition, 2014, pp. 1633 - 1638
Issue Date:
2014-01-01
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© 2014 IEEE. In this paper, we focus on developing a novel noise-robust LBP-based texture feature extraction scheme for texture classification. Specifically, two solutions have been proposed to overcome the primary two reasons that cause local binary pattern sensitive to noise. First, a hybrid model is proposed for noise-robust texture description. In this new model, the local primitive micro features are encoded with the texture's global spatial structure to reduce the noise sensitiveness. Second, we design an adaptive quantization algorithm, in which quantization thresholds are choosing adaptively on the basis of the texture's content. Higher noise-tolerance and discriminant power can be obtained in the quantization process. Based on the proposed hybrid texture description model and adaptive quantization algorithm, we develop an adaptive hybrid pattern scheme for noise-robust texture feature extraction. Compared with several state-of-the-art feature extraction schemes, our scheme leads to significant improvement in noisy texture classification.
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