Method of texture image segmentation based on SVM and gray level co-occurrence matrix

CHEN Ying1; YANG Feng-yu1; FU Xiang1

doi:10.13873/j.1000-97872012.09.026

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Method of texture image segmentation based on SVM and gray level co-occurrence matrix

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- Method of texture image segmentation based on SVM and gray level co-occurrence matrix
- Vol. 31, Issue 9, Pages: 60-63(2012)
- 作者机构：
  
  1. 南昌航空大学软件学院
  2. 吉林大学计算机科学与技术学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872012.09.026
  CLC： TP391.41
- Published：2012
- 稿件说明：
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[1]陈英,杨丰玉,符祥.基于支持向量机和灰度共生矩阵的纹理图像分割方法[J].传感器与微系统,2012,31(09):60-63.

CHEN Ying1, YANG Feng-yu1, FU Xiang1. Method of texture image segmentation based on SVM and gray level co-occurrence matrix[J]. 2012, 31(9): 60-63.
[1]陈英,杨丰玉,符祥.基于支持向量机和灰度共生矩阵的纹理图像分割方法[J].传感器与微系统,2012,31(09):60-63. DOI： 10.13873/j.1000-97872012.09.026.

CHEN Ying1, YANG Feng-yu1, FU Xiang1. Method of texture image segmentation based on SVM and gray level co-occurrence matrix[J]. 2012, 31(9): 60-63. DOI： 10.13873/j.1000-97872012.09.026.

摘要

图像分割是计算机视觉领域的研究热点。灰度共生矩阵是图像灰度的二阶统计度量

反映了纹理图像灰度在方向、局部邻域和变化幅度的综合信息

以能量、对比度、熵、差方差和差熵作为纹理图像的特征

利用支持向量机（SVM）对这些特征进行训练和分类

以达到纹理图像分割的目的。详细说明了纹理图像的分割过程

同时分析了不同参数情况下对分割精度的影响。针对Brodatz纹理库的实验结果表明:该方法具有较好的分割效果。

Abstract

Image segmentation is a hotspot in the field of computer vision.Gray level co-occurrence matrix is a second-order statistical measure of image grayscale which reflects the comprehensive information of image grayscale in the direction

local neighborhood and magnitude of changes

support vector machine（SVM） is used in training and classification of these features

which are energy

contrast

entropy

different variance and different entropy stand for textural image

to achieve the purpose of texture image segmentation.The process of texture image segmentation is explained

and the effect of different parameters on the segmentation precision is analyzed.The experimental results of Brodatz texture library show that this method has better segmentation effect.

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Keywords

references

形态小波域多尺度马尔可夫模型在纹理图像分割中的应用 [J]. 陈晓惠,郑晨,段汕,秦前清,胡亦钧. 中国图象图形学报 . 2011(05)

结合半局部信息与结构张量的无监督纹理图像分割 [J]. 赵在新,成礼智. 中国图象图形学报 . 2011(04)

基于结构张量和Wasserstein距离的纹理图像分割 [J]. 谢晓振,吴纪桃. 中国图象图形学报 . 2010(09)

基于分形特征纹理图像分割方法 [J]. 马时平,毕笃彦,黄文涛. 计算机应用 . 2004(06)

基于小波变换的纹理图像分割 [J]. 叶桦,章国宝,陈维南. 东南大学学报 . 1999(01)

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