邻域约束高斯混合模型的模糊聚类图像分割*

针对传统模糊聚类分割方法无法有效模拟数据分布特征的问题,提出基于邻域约束高斯混合模型的模糊聚类图像分割算法.利用高斯分布刻画聚类内像素光谱测度统计特征,定义像素与其邻域像素相关性的先验概率,并作为高斯混合模型中各高斯分量权重系数,构建包含特征场邻域作用的高斯混合模型.利用高斯分量描述像素与聚类间的非相似性测度,建立基于高斯混合模型的模糊聚类目标函数.在传统模糊聚类方法基础上,采用高斯混合模型定义像素与聚类间的非相似性测度,并在高斯混合模型中融入邻域作用,有效解决数据具有多峰值特征的问题.最后通过实验验证文中算法的准确性.

Fuzzy Clustering Image Segmentation Based on Neighborhood Constrained Gaussian Mixture Model

The characteristics of data can not be simulated in the traditional fuzzy clustering method effectively. Gaussian mixture model with neighbor constraints is introduced to solve the problem. Gaussian distribution is used to characterize the statistical characteristics of spectral measure. The correlation between the pixels and their neighborhood pixels are defined as prior probability and used as weight coefficients of each component in Gaussian mixture model. Finally, a Gaussian mixture model with neighborhood constraints in feature field is constructed. Log weighted Gaussian component in the mixture model is used as dissimilar measurement between the pixels and clusters, and a fuzzy clustering objective function is constructed based on Gaussian mixture model. Neighborhood constraints are introduced as a weight of component in traditional Gaussian mixture model and combined with fuzzy clustering method. Thus, the problem of multi-peak distribution of data is solved. Finally, the accuracy of the proposed algorithm is verified by experiments.