【图像分割】萤火虫算法图像聚类分割【含Matlab源码 2106期】.zip

%% Firefly Algorithm 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 FA 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 % Firefly Algorithm Parameters MaxIt = 100; % Maximum Number of Iterations nPop = 7; % Number of Fireflies (Swarm Size) gamma = 1; % Light Absorption Coefficient beta0 = 2; % Attraction Coefficient Base Value alpha = 0.2; % Mutation Coefficient alpha_damp = 0.98; % Mutation Coefficient Damping Ratio delta = 0.05*(VarMax-VarMin); % Uniform Mutation Range m = 2; if isscalar(VarMin) && isscalar(VarMax) dmax = (VarMax-VarMin)*sqrt(nVar); else dmax = norm(VarMax-VarMin); end % Start % Empty Firefly Structure firefly.Position = []; firefly.Cost = []; firefly.Out = []; % Initialize Population Array pop = repmat(firefly, nPop, 1); % Initialize Best Solution Ever Found BestSol.Cost = inf; % Create Initial Fireflies 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.Cost BestSol = pop(i); end end % Array to Hold Best Cost Values BestCost = zeros(MaxIt, 1); %% Firefly Algorithm Main Loop for it = 1:MaxIt newpop = repmat(firefly, nPop, 1); for i = 1:nPop newpop(i).Cost = inf; for j = 1:nPop if pop(j).Cost < pop(i).Cost rij = norm(pop(i).Position-pop(j).Position)/dmax; beta = beta0.*exp(-gamma.*rij^m); e = delta.*unifrnd(-1, +1, VarSize); %e = delta*randn(VarSize); newsol.Position = pop(i).Position ... + beta.*rand(VarSize).*(pop(j).Position-pop(i).Position) ... + alpha.*e; newsol.Position = max(newsol.Position, VarMin); newsol.Position = min(newsol.Position, VarMax); [newsol.Cost newsol.Out] = CostFunction(newsol.Position); if newsol.Cost <= newpop(i).Cost newpop(i) = newsol; if newpop(i).Cost <= BestSol.Cost BestSol = newpop(i); end end end end end % Merge pop = [pop newpop]; % Sort [~, SortOrder] = sort([pop.Cost]); pop = pop(SortOrder); % Truncate pop = pop(1:nPop); % Store Best Cost Ever Found BestCost(it) = BestSol.Cost; BestRes(it)=BestSol.Cost; disp(['Iteration ' num2str(it) ': Best Cost = ' num2str(BestCost(it))]); % Damp Mutation Coefficient alpha = alpha*alpha_damp; FACenters=Res(X, BestSol); end FAlbl=BestSol.Out.ind; % Plot FA Train figure; plot(BestRes,'--k','linewidth',2); title('FA Train'); xlabel('FA Iteration Number'); ylabel('FA Best Cost Value'); %% Converting cluster centers and its indexes into image Z=FACenters(FAlbl',:); 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');