用于图像分割的多分类高斯混合模型和基于邻域信息的高斯混合模型

高斯混合模型是一种简单有效且被广泛使用的图像分割工具。然而,传统的高斯混合模型在混合成分个数确定时的拟合结果不够精确；此外,由于没有考虑像素间的空间关系,导致分割结果易受噪声干扰,且分割精度不高。为弥补传统高斯混合模型的缺陷,文中提出多分类高斯混合模型和基于邻域信息的高斯混合模型用于图像分割。多分类高斯混合模型对传统混合模型进行二重分解:传统混合模型由M个分布加权混合得到,多分类混合模型进一步将M个分布中的每一个分布分解成R个分布。即多分类高斯混合模型由M个高斯分布混合组成,而这M个分布分别由R个不同的分布混合得到,提高了模型的拟合精度。基于邻域信息的高斯混合模型通过对模型中的先验概率和后验概率添加空间信息约束,增强了像素间的信息关联和抗噪性。采用结构相似性、误分率和峰值信噪比等指标来评价分割结果。通过实验发现:与现有的混合模型分割方法相比,文中方法大幅提高了分割精度,且有效地抑制了噪声干扰。

Multi-class Gaussian Mixture Model and Neighborhood Information Based Gaussian Mixture Model for Image Segmentation

Gaussian mixture model is one of the simple,effective and widely used tools in image segmentation.How-ever,the fitting result is not accurate enough when the number of mixture components in the traditional Gaussian mixture model is determined.In addition,because the spatial relationship between pixels is not considered,the segmentation results are easily affected by noise,and the segmentation accuracy is not high.To remedy the defects of the traditional Gaussian model,this paper proposed a multi-class Gaussianmixture model and a neighborhood information based Gaussianmixture model for image segmentation.The multi-class Gaussian mixture model decomposes the traditional mixture model.The traditional mixture model is composed of M different weighted distributions,and multi-class Gaussianmixture model decomposes each of the M components into R different distributions,that is,the multi-class Gaussian mixture model is composed of M different weighted distributions,and each of the M distributions is obtained by mixing R different distributions,thus improving the fitting accuracy of the model.The neighborhood information based Gaussianmixture model adds spatial information to the prior probability and posterior probability in the model,thus enhancing the information association and antinoise capability among pixels.The segmentation results were evaluated by the indexes of structural similarity,misclassification rate and peak signal-to-noise ratio.The experimental results show that compared with the existing segmentation method of mixture model,the segmentation accuracy of the proposed method in this paper is greatly improved,and the noise is effectively restrained.