Research On Meta-heuristic Optimization Algorithm And S_? Entropy In The Field Of Image Segmentation

For image processing and analysis,image segmentation is a crucial step.There are many methods of image segmentation.Among them,the thresholding method is a mature,efficient and easy to implement method.Multilevel thresholding mainly consists of two parts.One is to select an appropriate objective function to obtain the threshold,which directly determines the final image segmentation quality.The other is to choose the appropriate optimization algorithm to improve the efficiency of image segmentation.In this thesis,S? entropy is used as the objective function to obtain the threshold which is a new information entropy,and two new meta-heuristic optimization algorithms,Harris hawks optimization algorithm and multi-verse optimization algorithm are applied to optimize the process of obtaining the threshold.The main contributions and research contents of this paper are as follows:(1)A threshold segmentation method for images with S? entropy as the objective function is proposed.S? entropy was first proposed by Tsallis to discuss a possible'weakly correlated' system.When applied to image segmentation,it can be found that this entropy can significantly improve the quality of the segmented image.The experimental results also show its superiority over other objective functions.(2)Levy flight mechanism is introduced into the multiverse optimization algorithm(MVO),which improves the optimization ability of the optimization algorithm,and improves the efficiency of multi-level thresholding after applying it to multi-threshold segmentation of images.Levi's flight mechanism is considered to be a random mechanism that conforms to the random motion of organisms or particles in nature.The introduction of Levy-flight mechanism into the MVO can improve the population diversity of the optimization algorithm and avoid falling into local optimization.After comparing it with some traditional swarm optimization algorithms,such as particle swarm optimization algorithm and gray wolf algorithm,it can be noted that the improved MVO algorithm improves the convergence speed and optimization accuracy.(3)The Harris hawks optimization algorithm(HHO)was introduced into the multi-threshold segmentation of images.After finding that it converces slowly in the multi-level thresholding of images,the switching mechanism of exploration and exploitation of the algorithm was modified,and the ability of the algorithm in multithreshold optimization was improved.As the representative of the new meta-heuristic optimization algorithm,HHO has a complete structure,takes into account a variety of problems that may occur during optimization,and has a perfect switching mechanism from global search to local optimization,with high precision and good stability.After improving the switching function from global optimization to local optimization,the convergence rate of HHO is improved and the stability is higher.