【图像分割】基于樽海鞘算法的指数熵多阈值图像分割附Matlab代码

1 内容介绍

针对Otsu算法用于多阈值图像分割中存在运算时间长和精度低的不足,利用群智能优化算法对图像分割算法进行优化.本文首先应用樽海鞘群优化算法进行改进,将多阈值Otsu函数作为优化算法的适应度函数,利用SSA寻找适应度函数的最大值,同时获得相对应的多阈值.其次,通过对几幅基本图像、伯克利大学图像分割库中的图像和实际污油图像进行多阈值Otsu分割研究,在最佳适应度值、PSNR、SSIM指标以及算法耗时方面进行对比分析.实验结果表明本文提出的算法可以获得更为准确的分割阈值和更高的分割效率.

2 部分代码

%% 樽海鞘算法

function [FoodFitness,FoodPosition,Convergence_curve]=SSA(N,Max_iter,lb,ub,dim,fobj)

if size(ub,1)==1

ub=ones(dim,1)*ub;

lb=ones(dim,1)*lb;

end

Convergence_curve = zeros(1,Max_iter);

%Initialize the positions of salps

SalpPositions=initialization(N,dim,ub,lb);

FoodPosition=zeros(1,dim);

FoodFitness=inf;

%calculate the fitness of initial salps

for i=1:size(SalpPositions,1)

SalpFitness(1,i)=fobj(SalpPositions(i,:));

end

[sorted_salps_fitness,sorted_indexes]=sort(SalpFitness);

for newindex=1:N

Sorted_salps(newindex,:)=SalpPositions(sorted_indexes(newindex),:);

end

FoodPosition=Sorted_salps(1,:);

FoodFitness=sorted_salps_fitness(1);

Convergence_curve(1) = inf;

%Main loop

l=2; % start from the second iteration since the first iteration was dedicated to calculating the fitness of salps

while l<Max_iter+1

c1 = 2*exp(-(4*l/Max_iter)^2); % Eq. (3.2) in the paper

for i=1:size(SalpPositions,1)

SalpPositions= SalpPositions';

if i<=N/2

for j=1:1:dim

c2=rand();

c3=rand();

%%%%%%%%%%%%% % Eq. (3.1) in the paper %%%%%%%%%%%%%%

if c3<0.5

SalpPositions(j,i)=FoodPosition(j)+c1*((ub(j)-lb(j))*c2+lb(j));

else

SalpPositions(j,i)=FoodPosition(j)-c1*((ub(j)-lb(j))*c2+lb(j));

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

end

elseif i>N/2 && i<N+1

point1=SalpPositions(:,i-1);

point2=SalpPositions(:,i);

SalpPositions(:,i)=(point2+point1)/2; % % Eq. (3.4) in the paper

end

SalpPositions= SalpPositions';

end

for i=1:size(SalpPositions,1)

Tp=SalpPositions(i,:)>ub';Tm=SalpPositions(i,:)<lb';SalpPositions(i,:)=(SalpPositions(i,:).*(~(Tp+Tm)))+ub'.*Tp+lb'.*Tm;

SalpFitness(1,i)=fobj(SalpPositions(i,:));

if SalpFitness(1,i)<FoodFitness

FoodPosition=SalpPositions(i,:);

FoodFitness=SalpFitness(1,i);

end

end

Convergence_curve(l)=FoodFitness;

l = l + 1;

end

3 运行结果

4 参考文献

[1]邢致恺, 贾鹤鸣, 宋文龙. 基于莱维飞行樽海鞘群优化算法的多阈值图像分割[J]. 自动化学报, 2021.

部分理论引用网络文献，若有侵权联系博主删除。