Diplaros Aristeidis, Vlassis Nikos, Gevers Theo
Informatics Institute and with the Intelligent Sensory Information Systems, Faculty of Science, University of Amsterdam, Amsterdam 1098SJ, The Netherlands.
IEEE Trans Neural Netw. 2007 May;18(3):798-808. doi: 10.1109/TNN.2007.891190.
In this paper, we present a novel spatially constrained generative model and an expectation-maximization (EM) algorithm for model-based image segmentation. The generative model assumes that the unobserved class labels of neighboring pixels in the image are generated by prior distributions with similar parameters, where similarity is defined by entropic quantities relating to the neighboring priors. In order to estimate model parameters from observations, we derive a spatially constrained EM algorithm that iteratively maximizes a lower bound on the data log-likelihood, where the penalty term is data-dependent. Our algorithm is very easy to implement and is similar to the standard EM algorithm for Gaussian mixtures with the main difference that the labels posteriors are "smoothed" over pixels between each E- and M-step by a standard image filter. Experiments on synthetic and real images show that our algorithm achieves competitive segmentation results compared to other Markov-based methods, and is in general faster.
在本文中，我们提出了一种用于基于模型的图像分割的新颖空间约束生成模型和一种期望最大化（EM）算法。该生成模型假设图像中相邻像素的未观察到的类别标签由具有相似参数的先验分布生成，其中相似性由与相邻先验相关的熵量定义。为了从观测值估计模型参数，我们推导了一种空间约束EM算法，该算法迭代地最大化数据对数似然的下限，其中惩罚项依赖于数据。我们的算法非常易于实现，并且与高斯混合的标准EM算法类似，主要区别在于在每个E步和M步之间，标签后验通过标准图像滤波器在像素上进行“平滑”。在合成图像和真实图像上的实验表明，与其他基于马尔可夫的方法相比，我们的算法取得了具有竞争力的分割结果，并且通常速度更快。
