基于区域限制的EM和图割的非监督彩色图像分割方法

An Unsupervised Color Image Segmentation Method Based on Region-constrained EM and Graph cuts

提出一种基于区域限制的EM(Expectation Maximization)和图割的非监督彩色图像分割方法,以解决自动确定分割类数问题.首先,生成图像的超像素,提取图像的CIE Lab颜色特征和多尺度四元数Gabor滤波特征;为了高效自动地确定分割类数,同时避免因直接使用超像素造成的奇异值问题,对每一个超像素采样并使用采样像素表示超像素;然后采用高斯混合模型对采样像素集合进行建模,使用加入区域限制的分量EM自动获取模型组件数及参数,最后使用图割结合高斯混合模型对图像进行优化,获取最终分割结果.实验结果表明,该方法在分割效率和分割质量上均得到较大提升.

A new unsupervised color segmentation method based on region-constrained EM （Expectation Maximiza-tion）and graph cuts is proposed,which can automatically determine the number of segments for a color image.The pro-posed method first obtains the superpixels of the image and extracts CIE Lab color feature and multi-scale quaternion Gabor filter feature.In order to automatically and efficiently determine the number of segments for the image and avoid the problem caused by using superpixels directly,a window is used to sample each superpixel to obtain a pixel subset which represents the superpixel.Then the feature space of the sampled pixel subsets is modeled with Gaussian mixture model,and the model parameters （including the number of components）are obtained by a region-constraint component-wise EM algorithm.Final-ly,the segmentation result can be obtained by α-expansion with the learned model parameters.Experimental results demon-strate the good performance of the proposed method.