The Image Segmentation Based On Genetic Algorithm

Image segmentation, as the pretreatment of the pattern recognition and image analysis, as the key step of the image processing to image analysis, and also as one computer vision technology, is very important on image recognition and image analysis.Genetic Algorithm (GA) is a random search and optimization method based on nature selection and genetic mechanism of the living beings. In recent years, because of successfully solving the complex optimization and the industrial engineering problems, the research on GA has attracted a lot of attention.Genetic algorithm has the virtue of simpleness, robustness, parallel in essence and self-adaptability. The paper introduces selection operator,crossover operator,variance operator and new individuals, so the arithmetic avoids premature and improves convergent speed and capability. GA, as a kind of parallel computing, is huge in the potential to improve its computing speed because its optimization principle. And image segmentation needs higher computation cost and more time. So, in the image segmentation field, GA is usually used to get the threshold of image segmentation.General modal and threshold selection of the image segmentation are presented. In addition, the conceptions, realization process, mathematics basis, features, application and development of GA are given. Based on the optimization of GA, the image segmentation method using 2D maximum entropy based on genetic algorithm is proposed. GA is introduced into image segmentation to optimize the procedure of seeking threshold and to reduce the calculation and storage space using the characteristic of quick seeking virtue. The experiment results indicate that this method can get better effect.