一种多尺度灰度共生矩阵的纹理特征提取算法

王民; 王静; 王羽笙

doi:10.3788/YJYXS20163110.0967

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一种多尺度灰度共生矩阵的纹理特征提取算法

显示与图像 | 更新时间：2020-07-07

- 一种多尺度灰度共生矩阵的纹理特征提取算法
- Multi-scale algorithm of texture feature extraction based on gray-level co-occurrence matrix
- 液晶与显示 2016年31卷第10期页码：967-972
- 作者机构：
  
  西安建筑科技大学信息与控制工程学院, 陕西西安 710055
- 作者简介：
  
  王民(1959-), 男, 江苏常州人, 教授, 西安建筑科技大学通信与信息工程系主任, 电子信息工程教研室主任, 长期从事智能信息处理方面的工作, 数字语音处理、多媒体通信技术。E-mail:wangmin1329@163.com
- 基金信息：
  
  国家自然科学基金(61373112);住房城乡建设部科学技术项目计划(2016-R2-045);陕西省自然科学基金青年基金(2014JM8343);Natural Science Foundation of China(61373112);Housing and Urbsn Ministry of Science project(2016-R2-045);Shan'xi Province Natural Science Basic Research funding(2014JM8343)
- DOI：10.3788/YJYXS20163110.0967
  中图分类号： TP394.1;TH691.9
- 收稿日期：2016-05-20，
  
  录用日期：2016-7-19，
  
  纸质出版日期：2016-10
- 稿件说明：
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王民, 王静, 王羽笙. 一种多尺度灰度共生矩阵的纹理特征提取算法[J]. 液晶与显示, 2016,31(10):967-972.

Min WANG, Jing WANG, Yu-sheng WANG. Multi-scale algorithm of texture feature extraction based on gray-level co-occurrence matrix[J]. Chinese journal of liquid crystals and displays, 2016, 31(10): 967-972.
王民, 王静, 王羽笙. 一种多尺度灰度共生矩阵的纹理特征提取算法[J]. 液晶与显示, 2016,31(10):967-972. DOI： 10.3788/YJYXS20163110.0967.

Min WANG, Jing WANG, Yu-sheng WANG. Multi-scale algorithm of texture feature extraction based on gray-level co-occurrence matrix[J]. Chinese journal of liquid crystals and displays, 2016, 31(10): 967-972. DOI： 10.3788/YJYXS20163110.0967.

摘要

纹理特征作为图像的一个重要特征，在国画分类识别中的地位十分重要，但现有的纹理提取算法大多基于灰度信息而忽略了颜色信息。针对国画分类识别中纹理提取算法存在的问题，本文提出了一种多尺度、多色域的纹理特征提取算法，该算法结合了轮廓波变换和灰度共生矩阵的优点。为了对国画进行特征提取，该算法首先将国画图像转变到HSI色彩空间。然后，提取色调、饱和度、强度这三个色彩分量进行分区域操作，即提取每一个色彩分量的纹理特征。最后，将提取的3个特征向量融合并进行主成分分析降维。实验证明，与灰度共生矩阵相比，本文算法在国画分类识别方面查准率提高了7.5%，查全率提高了8.7%。实验表明多尺度灰度共生矩阵算法优于传统的灰度共生矩阵算法。

Abstract

As an important feature

texture feature is very important in the category of Chinese painting

but the majority of the existing texture extraction algorithms is based on gray-scale information. To solve the problems in Chinese painting texture extraction classification

a multi-scale

multi-color domain texture feature extraction algorithm has been proposed. This algorithm combines the advantages of Contourlet transform and Gray-level Co-occurrence Matrix. In order to extract Chinese painting features by the new algorithm

the image is first transformed into HSI color space. Then

the three color components of HSI is extracted to sub-regional operation

which is to extract the texture characteristics of each color component. Finally

three feature vectors are integrated and the dimensionality of matrix is reduced using Principal Component Analysis. Experimental results show that compared with Gray-level Co-occurrence Matrix

the algorithm improves the precision of 7.5%

re-check rate increased by 8.7% in terms of Chinese painting classification. The experiment show that the new algorithm presented in this paper is better than Gray-level Co-occurrence Matrix algorithm.

关键词

Keywords

references

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