【图像分割】基于麻雀算法优化Kmeans实现图像分割附Matlab代码

1 内容介绍

提出一种基于K-Means聚类的麻雀算法,该算法利用麻雀算法鲁棒性较强且不易陷入局部最优值的特点,动态的确定了聚类的数目和中心,解决了K-Means聚类初始点选择不稳定的缺陷,在此两种算法融合的基础上进行图像分割处理,经试验证明该算法效果理想.

2 仿真代码

%_________________________________________________________________________%

% 麻雀优化算法 %

%_________________________________________________________________________%

function [Best_pos,Best_score,curve]=SSA(pop,Max_iter,lb,ub,dim,fobj)

ST = 0.6;%预警值

PD = 0.7;%发现者的比列，剩下的是加入者

SD = 0.2;%意识到有危险麻雀的比重

PDNumber = pop*PD; %发现者数量

SDNumber = pop - pop*PD;%意识到有危险麻雀数量

if(max(size(ub)) == 1)

ub = ub.*ones(1,dim);

lb = lb.*ones(1,dim);

end

%种群初始化

X0=initialization(pop,dim,ub,lb);

X = X0;

%计算初始适应度值

fitness = zeros(1,pop);

for i = 1:pop

fitness(i) = fobj(X(i,:));

end

[fitness, index]= sort(fitness);%排序

BestF = fitness(1);

WorstF = fitness(end);

GBestF = fitness(1);%全局最优适应度值

for i = 1:pop

X(i,:) = X0(index(i),:);

end

curve=zeros(1,Max_iter);

GBestX = X(1,:);%全局最优位置

X_new = X;

for i = 1: Max_iter

BestF = fitness(1);

WorstF = fitness(end);

R2 = rand(1);

for j = 1:PDNumber

if(R2<ST)

X_new(j,:) = X(j,:).*exp(-j/(rand(1)*Max_iter));

else

X_new(j,:) = X(j,:) + randn()*ones(1,dim);

end

for j = PDNumber+1:pop

% if(j>(pop/2))

if(j>(pop - PDNumber)/2 + PDNumber)

X_new(j,:)= randn().*exp((X(end,:) - X(j,:))/j^2);

else

%产生-1，1的随机数

A = ones(1,dim);

for a = 1:dim

if(rand()>0.5)

A(a) = -1;

end

AA = A'*inv(A*A');

X_new(j,:)= X(1,:) + abs(X(j,:) - X(1,:)).*AA';

end

Temp = randperm(pop);

SDchooseIndex = Temp(1:SDNumber);

for j = 1:SDNumber

if(fitness(SDchooseIndex(j))>BestF)

X_new(SDchooseIndex(j),:) = X(1,:) + randn().*abs(X(SDchooseIndex(j),:) - X(1,:));

elseif(fitness(SDchooseIndex(j))== BestF)

K = 2*rand() -1;

X_new(SDchooseIndex(j),:) = X(SDchooseIndex(j),:) + K.*(abs( X(SDchooseIndex(j),:) - X(end,:))./(fitness(SDchooseIndex(j)) - fitness(end) + 10^-8));

end

%边界控制

for j = 1:pop

for a = 1: dim

if(X_new(j,a)>ub)

X_new(j,a) =ub(a);

end

if(X_new(j,a)<lb)

X_new(j,a) =lb(a);

end

%更新位置

for j=1:pop

fitness_new(j) = fobj(X_new(j,:));

end

for j = 1:pop

if(fitness_new(j) < GBestF)

GBestF = fitness_new(j);

GBestX = X_new(j,:);

end

X = X_new;

fitness = fitness_new;

%排序更新

[fitness, index]= sort(fitness);%排序

BestF = fitness(1);

WorstF = fitness(end);

for j = 1:pop

X(j,:) = X(index(j),:);

end

curve(i) = GBestF;

end

Best_pos =GBestX;

Best_score = curve(end);

end

3 运行结果

4 参考文献

[1]杨雨航. "动态粒子群优化K-means的图像分割算法研究." 现代计算机 8(2019):5.

[2]张宏峰, 倪受东, 赵亮,等. 基于麻雀搜索算法的摄像机标定优化方法[J]. 激光与光电子学进展, 2021, 58(22):2215004.

[3]李志杰, 王力, 张习恒. 改进樽海鞘群优化K-means算法的图像分割[J]. 包装工程, 2022, 43(9):10.

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