Bayesian image segmentation using hidden fields: Supervised, unsupervised, and semi-supervised formulations
Bayesian image segmentation using hidden fields: Supervised, unsupervised, and semi-supervised formulations
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使用隐藏域的贝叶斯图像分割：有监督、无监督和半监督公式
DOI：
10.1109/eusipco.2016.7760303
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Mário A. T. Figueiredo
中科院分区：
文献类型：
--
作者：
J. Bioucas;Mário A. T. Figueiredo
分割是图像分析中的核心问题之一，其目标是将图像域划分为具有某种同质性的区域。大多数情况下，划分是通过解决组合优化问题获得的，这通常是np困难的。在本文中，我们采用另一种方法，使用基于一组隐藏实值随机场的贝叶斯公式，这些随机场是划分的条件。这个公式产生一个连续的优化问题，而不是一个组合的问题。在有监督情况下，这个问题是凸的，我们用乘法器的交替方向法（ADMM）的一个实例来解决它。在无监督和半监督情况下，优化问题是非凸的，我们使用期望最大化（EM）算法来解决它，其中m步是通过ADMM实现的。仿真和实际数据的实验表明了该方法的有效性和灵活性。
Segmentation is one of the central problems in image analysis, where the goal is to partition the image domain into regions exhibiting some sort of homogeneity. Most often, the partition is obtained by solving a combinatorial optimization problem, which is, in general, NP-hard. In this paper, we follow an alternative approach, using a Bayesian formulation based on a set of hidden real-valued random fields, which condition the partition. This formulation yields a continuous optimization problem, rather than a combinatorial one. In the supervised case, this problem is convex, and we tackle it with an instance of the alternating direction method of multipliers (ADMM). In the unsupervised and semi-supervised cases, the optimization problem is nonconvex, and we address it using an expectation-maximization (EM) algorithm, where the M-step is implemented via ADMM. The effectiveness and flexibility of the proposed approach is illustrated with experiments on simulated and real data.