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基于金字塔分解和扇形局部均值二值模式的鲁棒纹理分类方法

宋铁成 罗林 张刚 罗忠涛 张天骐

宋铁成, 罗林, 张刚, 罗忠涛, 张天骐. 基于金字塔分解和扇形局部均值二值模式的鲁棒纹理分类方法[J]. 电子与信息学报, 2018, 40(6): 1301-1308. doi: 10.11999/JEIT170884
引用本文: 宋铁成, 罗林, 张刚, 罗忠涛, 张天骐. 基于金字塔分解和扇形局部均值二值模式的鲁棒纹理分类方法[J]. 电子与信息学报, 2018, 40(6): 1301-1308. doi: 10.11999/JEIT170884
SONG Tiecheng, LUO Lin, ZHANG Gang, LUO Zhongtao, ZHANG Tianqi. Robust Texture Classification Method Based on Pyramid Decomposition and Sectored Local Mean Binary Pattern[J]. Journal of Electronics and Information Technology, 2018, 40(6): 1301-1308. doi: 10.11999/JEIT170884
Citation: SONG Tiecheng, LUO Lin, ZHANG Gang, LUO Zhongtao, ZHANG Tianqi. Robust Texture Classification Method Based on Pyramid Decomposition and Sectored Local Mean Binary Pattern[J]. Journal of Electronics and Information Technology, 2018, 40(6): 1301-1308. doi: 10.11999/JEIT170884

基于金字塔分解和扇形局部均值二值模式的鲁棒纹理分类方法

doi: 10.11999/JEIT170884
基金项目: 

国家自然科学基金(61702065, 61671095),信号与信息处理重庆市市级重点实验室建设项目(CSTC2009CA2003)

Robust Texture Classification Method Based on Pyramid Decomposition and Sectored Local Mean Binary Pattern

Funds: 

The National Natural Science Foundation of China (61702065, 61671095), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009 CA2003)

  • 摘要: 针对传统局部二值模式(LBP)的特征鉴别力有限和噪声敏感性问题,该文提出一种基于金字塔分解和扇形局部均值二值模式的纹理特征提取方法。首先,将原始图像进行金字塔分解,得到对应于不同分解级别的低频和高频(差分)图像。为提取兼具鉴别力和稳健性的特征,进一步采用阈值化处理技术将高频图像转化为正、负高频图。然后,基于局部均值操作提出一种扇形局部均值二值模式(SLMBP),用于计算各级分解图像的纹理特征码。最后,对纹理特征码进行跨频带的联合编码和跨级别的直方图加权,从而获得最终的纹理特征。在公开的3个纹理数据库(Outex, Brodatz和UIUC)上进行分类实验,结果表明该文所提方法能够有效地提高纹理图像在无噪声环境和含高斯噪声环境下的分类精度。
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出版历程
  • 收稿日期:  2017-09-20
  • 修回日期:  2018-02-01
  • 刊出日期:  2018-06-19

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