1 内容介绍
为了提高图像的分割效果,提出一种萤火虫算法优化聚类的图像分割方法。获得最大聚类优化目标函数,采用萤火虫算法对目标函数进行求解,找到图像的最佳聚类个数,根据最佳聚类个数对图像进行分割,通过仿真实验对分割效果进行测试。结果表明,该方法可以迅速、准确找到最佳阈值,提高图像分割的准确度和抗噪性能,可以较好地满足图像分割实时性要求。
2 部分代码
%% Differential Evolution image color quantization using clustering
clear;
clc;
warning('off');
img=imread('r.jpg');
img=im2double(img);
% Separating color channels
R=img(:,:,1);
G=img(:,:,2);
B=img(:,:,3);
% Reshaping each channel into a vector and combine all three channels
X=[R(:) G(:) B(:)];
%% Starting DE Clustering
k = 6; % Number of Colors (cluster centers)
%---------------------------------------------------
CostFunction=@(m) ClusterCost(m, X); % Cost Function
VarSize=[k size(X,2)]; % Decision Variables Matrix Size
nVar=prod(VarSize); % Number of Decision Variables
VarMin= repmat(min(X),k,1); % Lower Bound of Variables
VarMax= repmat(max(X),k,1); % Upper Bound of Variables
% DE Parameters
MaxIt=100; % Maximum Iterations
nPop=k*2; % Population Size
%
beta_min=0.2; % Lower Bound of Scaling Factor
beta_max=0.8; % Upper Bound of Scaling Factor
pCR=0.2; % Crossover Probability
% Start
empty_individual.Position=[];
empty_individual.Cost=[];
empty_individual.Out=[];
BestSol.Cost=inf;
pop=repmat(empty_individual,nPop,1);
for i=1:nPop
pop(i).Position=unifrnd(VarMin,VarMax,VarSize);
[pop(i).Cost, pop(i).Out]=CostFunction(pop(i).Position);
if pop(i).Cost BestSol=pop(i); end end BestRes=zeros(MaxIt,1); % DE Body for it=1:MaxIt for i=1:nPop x=pop(i).Position; A=randperm(nPop); A(A==i)=[]; a=A(1); b=A(2); c=A(3); % Mutation beta=unifrnd(beta_min,beta_max,VarSize); y=pop(a).Position+beta.*(pop(b).Position-pop(c).Position); y=max(y,VarMin); y=min(y,VarMax); % Crossover z=zeros(size(x)); j0=randi([1 numel(x)]); for j=1:numel(x) if j==j0 || rand<=pCR z(j)=y(j); else z(j)=x(j); end end NewSol.Position=z; [NewSol.Cost, NewSol.Out]=CostFunction(NewSol.Position); if NewSol.Cost pop(i)=NewSol; if pop(i).Cost BestSol=pop(i); end end end % Update Best Cost BestRes(it)=BestSol.Cost; % Iteration disp(['In Iteration # ' num2str(it) ': Highest Cost IS = ' num2str(BestRes(it))]); DECenters=Res(X, BestSol); end DElbl=BestSol.Out.ind; % Plot DE Train figure; plot(BestRes,'--k','linewidth',2); title('DE Train'); xlabel('DE Iteration Number'); ylabel('DE Best Cost Value'); %% Converting cluster centers and its indexes into image Z=DECenters(DElbl',:); R2=reshape(Z(:,1),size(R)); G2=reshape(Z(:,2),size(G)); B2=reshape(Z(:,3),size(B)); % Attaching color channels quantized=zeros(size(img)); quantized(:,:,1)=R2; quantized(:,:,2)=G2; quantized(:,:,3)=B2; % Plot Results figure; subplot(1,2,1); imshow(img);title('Original'); subplot(1,2,2); imshow(quantized);title('Quantized Image'); 3 运行结果 4 参考文献 [1]吴鹏. 萤火虫算法优化最大熵的图像分割方法[J]. 计算机工程与应用, 2014. 博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机、雷达通信、无线传感器等多种领域的Matlab仿真，相关matlab代码问题可私信交流。 部分理论引用网络文献，若有侵权联系博主删除。 精彩内容
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