• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2013, Vol. 35 ›› Issue (2): 154-158.

• 论文 • 上一篇    下一篇

一种结合多特征的SVM图像分割方法

邓晓飞,徐蔚鸿   

  1. (长沙理工大学计算机与通信工程学院,湖南 长沙 410114)
  • 收稿日期:2011-12-19 修回日期:2012-03-01 出版日期:2013-02-25 发布日期:2013-02-25
  • 基金资助:

    国家教育部重点科研基金资助项目(208098);湖南省教育厅重点科研基金资助项目(07A056)

An SVM image segmentation method using multifeatures

DENG Xiaofei,XU Weihong   

  1. (College of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha 410114,China)
  • Received:2011-12-19 Revised:2012-03-01 Online:2013-02-25 Published:2013-02-25

摘要:

在分析了频域相位信息和纹理信息在表征图像特征方面的重要性之后,提出了一种结合相位一致和纹理特征的SVM图像分割方法。该方法将相位一致性统计特征、纹理特征和灰度特征一起组合成训练特征向量,采用支持向量机分类方法对图像进行分割。相对于传统方法,该方法提取的统计特征向量可以有效地反映图像边缘细节和纹理信息。实验结果表明,该方法比传统的SVM图像分割方法更有效,尤其适用于图像中目标区域的边缘对比度低和纹理信息丰富的情形。

关键词: 图像分割, 相位一致, 纹理特征, 支持向量机

Abstract:

Therefore, after analyzing the importance of frequency domain phase information and textural information in characterizing image features, a novel SVM image segmentation method is proposed using phase consistency and textural features. The new method combines phase consistency statistic characteristics, textural features and graylevel characteristics into a training eigenvector and segments image with SVM classification technique. Compared with the traditional method, the statistical eigenvectors extracted by the new method can reflect details of the edges of image and textural information effectively. The experimental results show that the new method is more effective than the traditional method for SVM image segmentation, especially in the situation where there is low edge contrast and rich textural information in the image's target area. 

Key words: image segmentation, phase consistency, textural features, SVM