Image thresholding segmentation based on oriented genetic algorithm and maximum entropy

Image thresholding segmentation based on entropy is classical method. Time cost of image thresholding segmentation method based on maximum entropy and enumeration is unacceptable, so that the genetic algorithms is adopted to improve efficiency. However, the performance of image thresholding segmentation based on traditional genetic algorithms is not satisfied because of the premature convergence. Therefore, we propose oriented genetic algorithm to increase speed and success rate. Oriented genetic algorithm includes an oriented crossover operator which directs generation of offspring. The blindness of genetic algorithm is reduced and efficiency of optimization is improved due to introducing oriented crossover operator. The proposed method is compared with enumeration method and standard genetic algorithm in image segmentation experiment. Experimental results show that performance of proposed method is better than traditional methods.