Statistical image modeling based texture feature extraction in dual-tree complex wavelet transform domain

被引：0
|
作者：
Yang P. [1 ,2 ]
Zhang F.-L. [1 ]
Yang Z.-J. [1 ]
机构：
[1] School of Information Engineering, Nanjing Audit University, Nanjing
[2] School of Information Engineering, Nanchang Hangkong University, Nanchang
来源：
Kongzhi yu Juece/Control and Decision | 2019年 / 34卷 / 07期
关键词：
Dual-tree complex wavelet transform; Generalized Gamma distribution; Generalized Von Mises distribution; Texture feature extraction;
D O I：
10.13195/j.kzyjc.2017.1712
中图分类号：
学科分类号：
摘要：
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. © 2019, Editorial Office of Control and Decision. All right reserved.
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页码：1492 / 1496
页数：4
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