Research on image segmentation technology based on genetic algorithms

Abstract

The purpose of this paper is to use genetic algorithm(GA) to process image with bottom noise, and the processing effect is improved through the improvement of GA. Combining with image segmentation, this paper expounds the working mechanism of GA and the design methods of main modules such as fitness calculation, selection, crossover and mutation, and gives the specific values of parameter setting. The key issues such as the relationship between generation gap and excellent individuals, the substitution relationship between individual among different generations, the selection method of intersection points and the selection of mutation positions, and the maintenance of population number are clarified. The image with bottom noise is processed by this algorithm, the results show that the target image can be separated from the background with noise by GA, but the processing time is 7.416 seconds. In order to improve the processing efficiency, the traditional algorithm is improved by adaptively adjusting the crossover probability and mutation probability of the population using evolutionary generation and individual fitness values. The same noise image is segmented by improved GA. The results show that the improved GA has better image segmentation effect, the processing time is only 0.751 seconds, the efficiency is improved by nearly 10 times.

Keywords

• genetic algorithms
• image segmentation
• background noise
• adaptive
• crossover