Image Split-and-merge Segmentation Algorithm Based on Information Bottleneck Method
LI De-don · Computer and Modernization · 2013
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.