Texture Classification Based on Multi-resolution Trace Transform

LI Ming; XING Dong-dong; WANG Yu-ling

doi:10.3969/j.issn.0372-2112.2019.04.024

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Texture Classification Based on Multi-resolution Trace Transform

更新时间：2025-07-16

- Texture Classification Based on Multi-resolution Trace Transform
- Acta Electronica Sinica Vol. 47, Issue 4, Pages: 962-969(2019)
- 作者机构：
  
  1. 南昌航空大学江西省图像处理与模式识别重点实验室,江西,南昌,330063
  2. 东华理工大学江西省放射性地学大数据技术工程实验室,江西,南昌,330013
  3. 南昌航空大学信息工程学院,江西,南昌,330063
  4. 南昌航空大学江西省图像处理与模式识别重点实验室,江西,南昌,330063
  5. 东华理工大学江西省放射性地学大数据技术工程实验室,江西,南昌,330013
  6. 南昌航空大学信息工程学院,江西,南昌,330063
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.04.024
  CLC： TP391.4
- Published Online：25 April 2019，
  
  Published：2019
- 稿件说明：
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LI Ming, XING Dong-dong, WANG Yu-ling. Texture Classification Based on Multi-resolution Trace Transform[J]. Acta Electronica Sinica, 2019, 47(4): 962-969.
DOI：

LI Ming, XING Dong-dong, WANG Yu-ling. Texture Classification Based on Multi-resolution Trace Transform[J]. Acta Electronica Sinica, 2019, 47(4): 962-969. DOI： 10.3969/j.issn.0372-2112.2019.04.024.

摘要

针对Trace变换提取的图像特征缺乏对纹理边缘信息描述和计算代价高的问题，利用小波变换对图像轮廓的表征优势，提出了多分辨率Trace变换并应用于纹理图像分类.首先，将小波变换引入到Trace变换中，对纹理图像进行非下采样小波变换，得到不同频率的低频特征子图及高频边缘子图；其次，在各级子图上进行一组泛函的Trace变换，获取纹理图像的融合特征，在获得图像边缘信息的同时避免了Trace变换不同泛函组合计算代价过高的问题；最后，把融合特征送入支持向量机对图像进行分类.实验结果表明，对图像采用多分辨率Trace变换提取的融合特征具有更好的纹理描述能力，相对于传统Trace变换及MCM等对比方法具有更高的鉴别性能，且在时间效率上相对于传统Trace变换有大幅提升.

Abstract

There is a problem that the image features extracted by trace transform lack description of texture edge information

and the computation cost is high

too.Based on the advantages of wavelet transform in image contour representation

a new fusion feature extraction algorithm

multi-resolution trace transform

is proposed and applied to texture image classification.Firstly

the wavelet transform was introduced in trace transform

low frequency feature sub images and high frequency edge sub images of texture images at different frequencies are obtained by using nonsubsampled wavelet transform.Then

we carried out a set of functional trace transform on each level sub images to obtain the fusion features of texture image

which not only obtains the edge information of the image

but also avoids the problem of high cost.Finally

the fusion features were fed into support vector machines to classify the images.The experiment results show that the fusion features of multi-resolution trace transformation have better texture description ability and achieves higher recognition rate than the original trace transform and MCM contrast method

the time efficiency is greatly improved compared to the traditional trace transform.

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