摘要
统计图像建模方法使用某种参数控制的分布模型来描述纹理及其特征,即参数估计是该类方法的核心问题。鉴于此,提出一种新的纹理特征提取方法,利用广义伽马分布和广义冯·米塞斯分布在图像的双树复小波域上进行统计建模,利用对数累积量法进行高效的参数估计完成纹理特征提取。在VisTex和Brodatz纹理库上进行分类实验,结果表明所提方法能够有效捕获图像的纹理特征,获取较高的识别率。
The statistical image modeling method uses some distributed model of parameter control to describe texture and its characteristics, and parameter estimation is the crucial issue of the method. In this paper, a novel texture feature extraction method is proposed, which adopts statistical image modeling with generalized Gamma distribution and generalized Von Mises distribution to extract texture features through logarithmic cumulants based parameter estimation in the dual-tree complex wavelet transform domain. Experimental results on VisTex and Brodatz databases show that the proposed method can effectively capture texture features of image, and achieve higher classification accuracy rate.
作者
杨鹏
张凡龙
杨章静
YANG Peng;ZHANG Fan-long;YANG Zhang-jing(School of Information Engineering, Nanjing Audit University, Nanjing 211815, China;School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China)
出处
《控制与决策》
EI
CSCD
北大核心
2019年第7期1492-1496,共5页
Control and Decision
基金
国家自然科学基金项目(61662048,61603192)
关键词
双树复小波变换
广义伽马分布
广义冯·米塞斯分布
纹理特征提取
dual-tree complex wavelet transform
generalized Gamma distribution
generalized Von Mises distribution
texture feature extraction
