【图像分割】基于人工蜂群算法实现图像分割matlab代码

1 简介为快速准确地将图像中目标和背景分离开来,将新型群体智能模型中的人工蜂群算法、二维直线交叉熵相结合,提出了一种图像分割新方法.该方法将图像阈值看成人工蜂群算法中的蜜源,利用信息熵和最大熵原理设计人工蜂群算法的适应度函数;通过采蜜蜂、侦查蜂和观察蜂的分工协作和信息共享,逐代逼近最佳阈值.并利用Matlab实现了图像分割算法,对分割的结果进行分析.实验结果表明,该方法在阈值分割图像时,蜂群算法能够快速准确地将图像目标分离出来,分离出来的目标更加适合后序的分析和处理.2 部分代码%%%%%ARTIFICIAL BEE COLONY ALGORITHM%%%% %Artificial Bee Colony Algorithm was developed by Dervis Karaboga in 2005 %by simulating the foraging behaviour of bees. %Copyright ?2008 Erciyes University, Intelligent Systems Research Group, The Dept. of Computer Engineering %Contact: %Dervis Karaboga (karabogaerciyes.edu.tr ) %Bahriye Basturk Akay (bahriyeerciyes.edu.tr) function [SO,TO] runABC(N,D,Iter,Pr,run) % clear all close all clc Iter 100; N 10; BEA 7; D 2; Pr0.5; run50; % load temp.mat imgimread(1.png); im rgb2gray(img); mat im; tic % Set ABC Control Parameters ABCOpts struct( ColonySize, N, ... % Number of Employed Bees Number of Onlooker Bees MaxCycles, Iter,... % Maximum cycle number in order to terminate the algorithm ErrGoal, 1e-20, ... % Error goal in order to terminate the algorithm (not used in the code in current version) Dim, D, ... % Number of parameters of the objective function Limit, 100, ... % Control paramter in order to abandone the food source lb, 1, ... % Lower bound of the parameters to be optimized ub, 256, ... %Upper bound of the parameters to be optimized ObjFun , Hgrey, ... %Write the name of the objective function you want to minimize RunTime,run); % Number of the runs GlobalMinszeros(ABCOpts.RunTime,ABCOpts.MaxCycles); for r1:ABCOpts.RunTime % Initialise population Range repmat((ABCOpts.ub-ABCOpts.lb),[ABCOpts.ColonySize 1]);%ABCOpts.Dim]); Lower repmat(ABCOpts.lb, [ABCOpts.ColonySize 1]);%ABCOpts.Dim]); XColony rand(ABCOpts.ColonySize,1) .* Range Lower; YColony rand(ABCOpts.ColonySize,1).* Range Lower; Colony [XColony , YColony]; size(Colony); Colony ceil(Colony); EmployedColony(1:(ABCOpts.ColonySize/2),:); %evaluate and calculate fitness ObjEmpfeval(ABCOpts.ObjFun,Employed,mat) %fprintf(obj %d \t,ObjEmp); %fprintf(\n); %ObjEmp Location(pos,Employed); %display(ObjEmp); FitEmpcalculateFitness(ObjEmp); display(FitEmp); %fprintf(%d \t,FitEmp); %set initial values of Bas Baszeros(1,(ABCOpts.ColonySize/2)); % % fprintf(Cycle%d ObjVal%g\n,Cycle,GlobalMin); % %Bestcycle(Cycle,:) CycleBestParams; %Bestcyval(Cycle) CycleMin; CycleCycle1; % end % End of s Bestcycle(r,:) CycleBestParams; Bestcyval(r) CycleMin; end; %end of runs toc %semilogy(mean(GlobalMins)) % Global GlobalMins; % Cyc rand()* ABCOpts.MaxCycles; % for r 1: 5 % for C 1: Cyc % Global(r,C) Global(r,C) * rand(); % end % so sort(Global(r,:)); % end %so sort(Global); %Gls unique(Globals) %Glt unique(Globalt) [c ,i] max(GlobalMins) [cc , ro] max(c) co i(ro) SO Globals(co,ro) TO Globalt(co,ro) plot(GlobalMins(1,:)); title(Mean of Best function values); xlabel(cycles); ylabel(Mean Values); fprintf(Mean %g Std%g\n,mean(GlobalMins(:,end)),std(GlobalMins(:,end))); S uint8(SO); [r,c] size(im); % whos S %wim whiten(uint8(gimg),uint8(S)); thr S / 256; %wim im2bw(gimg); img zeros(r,c); for i 1:r for j 1:c if im(i,j) S img(i,j) 1; end end end figure,imshow(mat2gray(img))3 仿真结果4 参考文献部分理论引用网络文献若有侵权联系博主删除。[1]霍凤财等. 基于人工蜂群算法的图像阈值分割. 自动化技术与应用 035.002(2016):112-116.