Method to reduce over-segmentation of images using immune clonal algorithm

摘要

Image segmentation is a difficult task. These years, researchers have proposed many segmentation methods based on Evolutionary Algorithms, but most of them used Evolutionary Algorithms to optimize the parameters of an existing segmentation algorithm. This paper tries to use the Evolutionary Algorithms to segment images expecting to explore a new way of image segmentation. The method described in the paper pre-segments the image by Watersheds and then merges it by Immune Clonal Algorithm (ICA). To implement the task, several operators are proposed such as the DC operator, the Proportional Creation of the First generation operator, and fitness function based on JND and average gray value. In the end, the proposed method is compared with another method using GA. The experiments show that the method is effective and the work is significant.