Multi Threshold Image Segmentation Based on General Learning Equalization Optimizer
The traditional meta-heuristic multi-threshold image segmentation algorithm has high computational complexity and is easy to fall into local optimum,whereas the general learning equilibrium optimizer enables particles to learn from candidate particles in different dimensions during the search process having a strong ability in solving the optimal solution of complex problems and overcoming the prob-lem of easily falling into local optimum. The general learning equalization optimization algorithm is proposed to optimize the maximum in-ter-class variance method to realize multi-threshold image segmentation,and the standard grayscale images are selected in the experiment. Taking the peak signal-to-noise ratio,structural similarity,running time and fitness value as the evaluation criteria,the algorithm is com-pared with the equalization optimization algorithm and the particle swarm optimization algorithm. The results show that the peak signal-to-noise ratio and structural similarity of the multi-threshold image segmentation algorithm based on the general learning equalization optimi-zer are better than the other two algorithms in most cases,the convergence speed is fast and the execution efficiency is high.
万方数据
维普
