🍁🥬🕒摘要🕒🥬🍁
图像分割将图像细分为构成它的子区域或物体。分割的目的是把特定的目标从复杂的图像中抽取出来,是图像识别,图像理解以及图像分析的重要依据。随着技术的发展图像分割已经广泛的使用在众多领域,如医学图像处理,人脸识别,交通道路分析等,分割的好坏直接影响后续工作的有效性。因此,越来越多的学者研究各种相关的图像分割算法,模糊理论能很好的描述图像的特征,在图像分割中得到深入研究。
萤火虫算法是受自然界中的萤火虫通过荧光进行信息交流这种群体行为的启发演变而来。作为一种新颖的仿生群智能优化算法,分析了萤火虫算法的仿生原理,从数学角度对算法实现优化过程进行了定义。通过典型的函数优化和组合优化问题对算法进行了仿真测试,测试结果表明了萤火虫算法在连续空间和离散空间优化的可行性和有效性,具有良好的应用前景。
✨🔎⚡部分运行结果⚡🔎✨
💂♨️👨🎓Matlab代码👨🎓♨️💂
%% Firefly Algorithm (FA) Image Segmentation Using Clustering clear; clc; warning('off'); % Loading img=imread('f.jpg'); img=im2double(img); gray=rgb2gray(img); gray=imadjust(gray); % Reshaping image to vector X=gray(:); %% Starting FA Clustering k = 6; % Number of clusters %--------------------------------------------------- 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 = 10; % 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; end FAlbl=BestSol.Out.ind; % Plot FA Train figure; plot(BestRes,'--k','linewidth',1); title('FA Train'); xlabel('FA Iteration Number'); ylabel('FA Best Cost Value'); %% Converting cluster centers and its indexes into image gray2=reshape(FAlbl(:,1),size(gray)); segmented = label2rgb(gray2); % Plot Results figure; subplot(1,2,1); imshow(img);title('Original'); subplot(1,2,2); imshow(segmented,[]);title('Segmented Image');
📜📢🌈参考文献🌈📢📜
[1]刘长平,叶春明.一种新颖的仿生群智能优化算法:萤火虫算法[J].计算机应用研究,2011,28(09):3295-3297.
[2]陈恺,陈芳,戴敏,张志胜,史金飞.基于萤火虫算法的二维熵多阈值快速图像分割[J].光学精密工程,2014,22(02):517-523.
