结合图像局部信息的高斯混合型图像分割框架

针对现有的基于判别型或聚类型的图像，用分割方法无法处理被噪声污染的图像的现状，提出一种新的两步式图像分割框架。该框架首先利用图像的局部信息重塑图像的灰度直方图，增强了像素的类间散布性和类内紧凑性，然后将现有的基于判别型或基于聚类型图像分割方法在重塑图像上执行，从而提高了现有图像分割算法的有效性和鲁棒性。文中用典型的聚类型方法高斯混合模型来说明该框架的可行性。由于框架的两个步骤具有独立性．因此可推广到现有的其他基于像素或直方图的方法。在人工和真实图像上的实验结果证明，这种两步图像分割框架可以获得有效且鲁棒的图像分割结果。

EFFECTIVE IMAGE SEGMENTATION FRAMEWORK FOR GAUSSIAN MIXTURE MODEL INCORPORATING LOCAL INFORMATION

A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the second step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model(GMM) and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.