An noise-robust adaptive hybrid pattern for texture classification

作者：Zhu, Ziqi^*; You, Xinge; Chen, C. L. Philip; Tao, Dacheng; Jiang, Xiubao; You, Fanyu; Zou, Jixing

来源：22nd International Conference on Pattern Recognition (ICPR), 2014-08-24 To 2014-08-28.

DOI：10.1109/ICPR.2014.289

• Adaptive hybrid pattern
• noise-robust
• texture classification

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 microfeatures 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.

• 出版日期2014
• 单位司法部司法鉴定科学技术研究所; 澳门大学; 华中科技大学