Multi-threshold image segmentation method based on comprehensive learning differential evolution algorithm

Abstract

The invention discloses a multi-threshold image segmentation method based on a comprehensive learning differential evolution algorithm. The method comprises the steps that in the mutation operation process of the differential evolution algorithm, a Binary tournament selection method is utilized to select an individual from species at random, a comprehensive individual is generated by the individual and an optimal individual, then the comprehensive individual serves as a basic individual, a mutation operation is carried out on the basic individual to generate a mutation individual, the searching speed is accelerated as fast as possible while the population diversity is kept, and then a crossover operation operator and a selection operation operator of a traditional differential evolution algorithm are carried out. Meanwhile, a zoom factor value and a crossover probability value are adjusted adaptively according to current search feedback information, so that the robustness of the algorithm is reinforced. The steps are repeatedly executed until a terminal condition is met, and the optimal individual obtained in the computation process is a final segmentation threshold of an image. By means of the multi-threshold image segmentation method based on the comprehensive learning differential evolution algorithm, the probability of local optimum can be reduced, the image segmentation accuracy is improved, the segmentation speed is accelerated, and the real time performance of the segmentation is improved.

Description

technical field [0001] The invention relates to digital image segmentation technology, in particular to a multi-threshold image segmentation method based on comprehensive learning differential evolution algorithm. Background technique [0002] Multi-threshold image segmentation is a very important digital image processing method in modern digital image processing. In the process of multi-threshold image segmentation, usually according to the preset segmentation criteria, multiple thresholds are found to identify the interesting part in the image, so as to segment the image into several different parts. In the multi-threshold image segmentation process, the most critical step is how to quickly and effectively optimize each threshold. However, the traditional multi-threshold image segmentation methods based on exhaustive search often have the disadvantages of slow search speed and low real-time performance, especially when the number of thresholds is large, the time-consuming...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the shortcomings of the traditional differential evolution algorithm for multi-threshold image segmentation, it is easy to fall into local optimum, the segmentation accuracy is not high, the segmentation speed is slow and the real-time performance is not strong, and a multi-threshold image based on comprehensive learning differential evolution algorithm is proposed. Segmentation method, in the process of mutation operation of differential evolution, a binary tournament selection method is used to randomly select an individual from the population, and generate an integrated individual with the optimal individual, and then execute based on the integrated individual The mutation operation generates mutated individuals, so as to speed up the search speed as much as possible while maintaining the diversity of the population, and then executes the hybridization and selection operation operators of the traditional differential evolution algorithm; at the same time, adaptively adjusts the scaling factor and The value of the hybridization probability, so as to enhance the robustness of the algorithm; repeat the above steps until the termination condition is met, and the optimal individual obtained in the calculation process is the final segmentation threshold of the image; compared with similar methods, the present invention can Reduce the probability of falling into local optimum, improve the accuracy of image segmentation, speed up segmentation, and improve the real-time performance of segmentation

Images