Image Segmentation Using Information Bottleneck Method
Image Segmentation Using Information Bottleneck Method
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DOI：
10.1109/tip.2009.2017823
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发表时间：
2009-07-01
影响因子：
10.6
通讯作者：
Sbert, Mateu
中科院分区：
文献类型：
--
作者：
Bardera, Anton;Rigau, Jaume;Sbert, Mateu
在图像处理中，分割算法是研究的主要焦点之一。本文提出了一种新的图像分割算法的基础上的硬版本的信息瓶颈方法。该方法的目的是提取一个变量的紧凑表示，认为输入，与另一个变量的互信息损失最小，认为输出。首先，我们介绍了一个分裂和合并算法的基础上定义的信息通道之间的一组区域（输入）的图像和强度直方图箱（输出）。从这个通道，最大化的互信息增益被用来优化图像分割。然后，在前一阶段中获得的区域的合并过程中进行的互信息的损失最小化。从上述通道的反转，我们还提出了一种新的直方图聚类算法的基础上最小化的互信息损失，其中现在的输入变量表示直方图箱和输出是从上述分裂和合并算法获得的区域集。最后，我们介绍了两个新的聚类算法，展示了如何将信息瓶颈方法应用于两个多模态图像正确对齐时获得的配准通道。在二维和三维图像上的不同实验显示了所提出的算法的行为。
In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms.