基于信息瓶颈法的图像分离-合并分割算法

Image Split-and-merge Segmentation Algorithm Based on Information Bottleneck Method

在图像处理中,分割算法是其主要研究焦点之一。针对该问题,提出基于信息瓶颈法的新图像分离-合并分割算法。该方法的目的是抽取与输入相关的一个变量的紧密表征,并使得在考虑与输出相关的另一个变量互信息的损失最小。首先,基于一系列图像区域和强度直方图集合之间定义信息渠道,在此渠道中,以互信息的最大化来优化图像分割法;然后,通过最小化互信息损失,完成在上一阶段中获得的多区域合并过程。在二维图像上做的实验表明所提出算法的性能。

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image split-and-merge 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, the algorithm is 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. Different experiments on 2-D images show the behavior of the proposed algorithm.