Digital image consists of pixels, in which the pixels that are different intensity belong to different regions. Sequentially, different shapes are displayed, while different shapes contains of different entropy. Therefore, image entropy can describe shape. For a image, assume that image intensity is nonnegative, that is , then we define image entropyas follow, ,where,. When an image has the equivalent probability of every intensity, the uncertainty of shape in image will reach its max, that is, the image contains the maximum entropy.
Published in Chapter:
Immune Programming Applications in Image Segmentation
Xiaojun Bi (Harbin Engineering University, P.R. China)
Copyright: © 2009
|Pages: 18
DOI: 10.4018/978-1-60566-310-4.ch014
Abstract
In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete the task of image segmentation. Traditional evolutionary algorithm represented by Genetic Algorithm is an efficient approach for image segmentation, but in the practical application, there are many problems such as the slow convergence speed of evolutionary algorithm and premature convergence, which have greatly constrained the application. The goal of introducing immunity into the existing intelligent algorithms is to utilize some characteristics and knowledge in the pending problems for restraining the degenerative phenomena during evolution so as to improve the algorithmic efficiency. Theoretical analysis and experimental results show that immune programming outperforms the existing optimization algorithms in global convergence speed and is conducive to alleviating the degeneration phenomenon. Theoretical analysis and experimental results show that immune programming has better global optimization and outperforms the existing optimization algorithms in alleviating the degeneration phenomenon. It is a feasible and effective method of image segmentation.