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Expectation-maximization algorithm with total variation regularization for vector-valued image segmentation

机译：向量值图像分割的总变化正则化期望最大化算法

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摘要

We integrate the total variation (TV) minimization into the expectation-maximization (EM) algorithm to perform the task of image segmentation for general vector-valued images. We first propose a unified var-iational method to bring together the EM and the TV regularization and to take advantages from both approaches. The idea is based on operator interchange and constraint optimization. In the second part of the paper we propose a simple two-phase approach by splitting the above functional into two steps. In the first phase, a typical EM method can classify pixels into different classes based on the similarity in their measurements. However, since no local geometric information of the image has yet been incorporated into the process, such classification in practice gives unsatisfactory segmentation results. In the second phase, the TV-step obtains the segmentation of the image by applying a TV regularization directly to the clustering result from EM.
机译：我们将总变化（TV）最小化集成到期望最大化（EM）算法中，以执行一般矢量值图像的图像分割任务。我们首先提出一种统一的变分方法，将EM和TV正则化结合在一起，并从这两种方法中受益。这个想法基于操作员互换和约束优化。在本文的第二部分中，我们通过将上述功能分为两个步骤，提出了一种简单的两阶段方法。在第一阶段，典型的EM方法可以根据像素的测量相似度将其分类为不同的类别。但是，由于尚未将图像的局部几何信息纳入该过程，因此这种分类在实践中给出的分割结果不令人满意。在第二阶段，TV步骤通过将TV正则化直接应用于来自EM的聚类结果来获得图像的分割。

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