Image Segmentation Based on Optimal Histogram Threshold by Improved Genetic Algorithms

In order to get optimal global solution and avoid prematurity a fitness normalization formula is introduced and it always gets a positive value. The new formula can guide the population to a proper direction and increase the press for selection of individuals. The similarity is defined to increase the varieties of individuals without increasing the size of population, thus solving the problem of local optimized solution. In order to solve the problem how to segment an image, an image segmentation method based on improved genetic algorithms is proposed. The method can find out the optimal threshold of the segmentation object by the Otsu formula. Different calculated results are obtained with different improved methods. Different segmented images are given by different segmentation methods.