Image Threshold Segmentation Based on Auxiliary Individual Oriented Crossover Genetic Algorithm

Abstract

Image threshold segmentation based on entropy is classical method. The time cost of applying the two-dimensional maximum entropy and enumeration threshold segmentation method is unacceptable, so that the genetic algorithms is adopted to improve efficiency. Because of the premature convergence of traditional genetic algorithm, the performance of image threshold segmentation is constrained. We propose a 2-D maximum entropy threshold segmentation method based on the auxiliary individual oriented crossover genetic algorithm (AIOXGA) to improve the speed and success rate of image threshold segmentation. The introduction of the AIOX operator reduces the blindness of the genetic algorithm and improves the optimization efficiency. This method was compared with enumeration method, standard genetic algorithm and original oriented genetic algorithm(OGA) in image segmentation experiments. The results show that the performance of this method is better than that of traditional methods.