The Two Step Mutation Evolutionary Programming Using in Image Segmentation

Abstract

Image segmentation is an important task in image analysis and processing. Many of the existing methods for segmenting a multi-component image (satellite or aerial) are very slow and require a priori knowledge of the image that could be difficult to obtain. Furthermore, the success of each of these methods depends on several factors, such as the characteristics of the acquired image, resolution limitations, intensity in-homogeneities and the percentage of imperfections induced by the process of image acquisition. Evolutionary programming(EP) has been applied with success to many numerical and combinatorial optimization problems in recent years. EP has rather slow convergence rates, however, on some function optimization problems. In this paper the new evolutionary programming is proposed to overcome the premature convergence. There are two step mutation in the new evolutionary programming. The first step is responsible for searching the whole space. The second is responsible for searching the local part in detail. The cooperation and specialization between different two step mutation are considered during the algorithm design. The new evolutionary programming can use in image segmentation and the experimental results show the new evolutionary programming is efficient.