【聚类分割】基于 K-means 聚类算法实现图像区域分割附matlab代码
【聚类分割】基于 K-means 聚类算法实现图像区域分割附matlab代码
TT_Matlab
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，完整matlab代码或者程序定制加qq1575304183。
1 简介
对图像进行颜色区域分割.将图像转换到CIE L a b颜色空间,用K均值聚类分析算法对描述颜色的a 和b 通道进行聚类分析;通过提取各个颜色区域独立成为单色的新图像,对图像进行分割处理.实验结果表明,在CIE L a b空间使用K—means聚类算法可以有效地分割彩色纺织品图像的颜色区域.
2 完整代码
clear all; close all; clc;
A = double(imread(
’bird_small.tiff’
));% 载入图片
dim = size(A,
1
); % 图片行数
k =
16
; % 颜色分类的层数
means = zeros(k,
3
); % Initialize means to randomly-selected colors
in
the original photo.
rand_x = ceil(dim*rand(k,
1
));%初始means是k行k列随机数作为聚类中心
rand_y = ceil(dim*rand(k,
1
));
for
i =
1
:k
means(i,
:
) = A(rand_x(i), rand_y(i),
:
);%在图像中找到初始聚类中心
end
for
itr=
1
:
100
s_x=zeros(k,
3
);
s_ind=zeros(k,
1
);
for
i=
1
:dim
for
j=
1
:dim
r=A(i,j,
1
);g=A(i,j,
2
);b=A(i,j,
3
);
[val ind]=min(sum((repmat([r,g,b],k,
1
)-means).^
2
,
2
));
%repmat(A,k,
1
)对A矩阵进行k行的复制
s_x(ind,
:
)=s_x(ind,
:
)+[r,g,b];
s_ind(ind)=s_ind(ind)+
1
;
end
end
for
ii=
1
:k
if
s_ind(ii)>
0
s_x(ii,
:
)=s_x(ii,
:
)./s_ind(ii);
end
end
d=sum(sqrt(sum((s_x-means).^
2
,
2
)));%计算距离
if
d<
1
e-
5
break
end
means=s_x;
end
means = round(means);
itr
figure; hold on
for
i=
1
:k
col = (
1
/
255
).*means(i,
:
);
rectangle(
’Position’
, [i,
0
,
1
,
1
],
’FaceColor’
, col,
’EdgeColor’
, col);
end
axis off
large_image = double(imread(
’bird_large.tiff’
));
figure;subplot(
121
);imshow(A,[]);title(
’原图’
)
large_dim = size(large_image,
1
);
for
i =
1
:large_dim
for
j =
1
:large_dim
r = large_image(i,j,
1
); g = large_image(i,j,
2
); b = large_image(i,j,
3
);
[val ind]=min(sum((repmat([r,g,b],k,
1
)-means).^
2
,
2
));
large_image(i,j,
:
) = means(ind,
:
);
end
end
subplot(
122
);imshow(uint8(round(large_image)));title(
’Kmean分割图’
)
imwrite(uint8(round(large_image)),
’bird_kmeans.jpg’
);% Save image
3 仿真结果
4 参考文献
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。