如何利用灰狼算法优化Renyi熵进行图像多阈值分割？

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Renyi 范围法因其显著效率而广泛用于图像分割。为了更好地发挥 Renyi 范围在图像分割中的应用，提出了将 Renyi 范围法扩展到图像多级阈值化问题。然而，由于计算时间复杂度高，存在局限性。

1 内容介绍

在图像阈值分割方法中,Renyi熵法因其显著效能而得到大量应用.为了更好地发挥Renyi熵在图像分割中的应用,提出把Renyi熵法扩展到图像多级阈值化问题.然而,由于计算时间复杂度上的高要求,很难把这种有效的技术推广到复杂图像多级阈值化问题.为减少本方法的计算时间,应用灰狼优化算法实施最佳阈值的搜索.实验结果表明,本方法能有效地对图像进行多级分割,并且显著降低计算时间.

2 部分代码

% Grey Wolf Optimizer

function [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fhandle,fnonlin)

% initialize alpha, beta, and delta_pos

Alpha_pos=zeros(1,dim);

Alpha_score=inf; %change this to -inf for maximization problems

Beta_pos=zeros(1,dim);

Beta_score=inf; %change this to -inf for maximization problems

Delta_pos=zeros(1,dim);

Delta_score=inf; %change this to -inf for maximization problems

%Initialize the positions of search agents

Positions=initialization(SearchAgents_no,ub,lb);

Convergence_curve=zeros(1,Max_iter);

l=0;% Loop counter

% Main loop

while l<Max_iter

for i=1:size(Positions,1)

% Return back the search agents that go beyond the boundaries of the search space

Flag4ub=Positions(i,:)>ub;

Flag4lb=Positions(i,:)<lb;

Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;

%% Calculate objective function for each search agent

fitness=Fun(fhandle,fnonlin,Positions(i,:));

%% Update Alpha, Beta, and Delta

if fitness<Alpha_score

Alpha_score=fitness; % Update alpha

Alpha_pos=Positions(i,:);

end

if fitness>Alpha_score && fitness<Beta_score

Beta_score=fitness; % Update beta

Beta_pos=Positions(i,:);

end

if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score

Delta_score=fitness; % Update delta

Delta_pos=Positions(i,:);

end

end

a=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0

% Update the Position of search agents including omegas

for i=1:size(Positions,1)

for j=1:size(Positions,2)

r1=rand(); % r1 is a random number in [0,1]

r2=rand(); % r2 is a random number in [0,1]

A1=2*a*r1-a; % Equation (3.3)

C1=2*r2; % Equation (3.4)

D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1

X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1

r1=rand();

r2=rand();

A2=2*a*r1-a; % Equation (3.3)

C2=2*r2; % Equation (3.4)

D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2

X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2

r1=rand();

r2=rand();

A3=2*a*r1-a; % Equation (3.3)

C3=2*r2; % Equation (3.4)

D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3

X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3

Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)

end

end

l=l+1;

Convergence_curve(l)=Alpha_score;

end

3 运行结果

4 参考文献

[1]聂方彦, 张平凤, 潘梅森,等. 基于Renyi熵与PSO算法的图像多级阈值分割[J]. 湖南文理学院学报：自然科学版, 2013, 25(3):6.

部分理论引用网络文献，若有侵权联系博主删除。