基于多尺度小波的纹理分析

系统仿真学报 ›› 2015, Vol. 27 ›› Issue (9): 1951-1959.

• 建模与仿真理论及方法 • 上一篇下一篇

基于多尺度小波的纹理分析

廖宁¹, 徐丽莎¹, 钱晓山²

1.中南林业科技大学涉外学院,湖南长沙 410004;
2.宜春学院物理科学与工程技术学院,江西宜春 336000

收稿日期:2015-04-18 修回日期:2015-07-22 出版日期:2015-09-08 发布日期:2020-08-07

Texture Classification Based on Multi-scale Wavelet

Liao Ning¹, Xu Lisha¹, Qian Xiaoshan²

1. Shewai School, Central South University of forestry and Technology, Changsha 410004, China;
2. Physical Science and Technology College, Yichun University, Yichun 336000, China

Received:2015-04-18 Revised:2015-07-22 Online:2015-09-08 Published:2020-08-07
About author:Liao ning(1982-), female, Hunan, Graduate, Lecturer, Research direction: Intelligent information processing; Xu Lisha(1982-), female, Hunan, Graduate, Lecturer, Research direction: Intelligent information processing.
Supported by:
The National Natural Science Foundation of China (0634020, 60874069, 60804037); 863 projects (2006AA04Z181); Yichun University research projects (XJ1314)

摘要/Abstract

摘要： 纹理分析对于旋转来说是非常敏感的。提出一种称为旋转不变的轮廓波傅立叶变换方法,通过提取子集香农熵来实现纹理分析的旋转不变性,对每个子集香农熵进行离散傅得到旋转不变的特征矢量,再利用DFT幅度谱的对称性进一步降低唯数。采用欧几里德距离和支持向量机进行纹理图像分类,1 500组纹理图像的实验结果表明,轮廓波变换是用来代表纹理方向和和旋转不变纹理特征的有效工具,并且在实现准确的分类同时其计算复杂度大大降低。

关键词: 多尺度小波, 离散傅立叶变换, 信息熵, 支持向量机, 纹理分类

Abstract: Texture analysis is quite sensitive to rotations. An efficient approach, called Invariant Contourlet-Fourier Descriptor, was proposed to achieve rotation invariance in texture analysis by extracting a set of Shannon entropy in contourlet domain. Discrete Fourier Transform analysis was applied to entropy vectors of each scale to form rotation invariant feature vectors, the dimensionality of which was reduced further due to the symmetry of DFT magnitude spectrum. Two classifiers, including the well-known Euclidean distance and Support Vector Machine, were studied to measure the distance between the known and unknown features. Experimental results on 1500 texture images show that contourlet is an efficient tool to represent directional texture, and the rotation invariant texture features were effective to achieve accurate classification with low computational complexity.

Key words: multi-scale wavelet, discrete fourier transform, shannon entropy, support vector machine, texture classification.

中图分类号:

TP18

廖宁, 徐丽莎, 钱晓山. 基于多尺度小波的纹理分析[J]. 系统仿真学报, 2015, 27(9): 1951-1959.

Liao Ning, Xu Lisha, Qian Xiaoshan. Texture Classification Based on Multi-scale Wavelet[J]. Journal of System Simulation, 2015, 27(9): 1951-1959.

参考文献

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基于多尺度小波的纹理分析

Texture Classification Based on Multi-scale Wavelet

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