display output k-means clustering, display output clustering as a image
显示 更早的评论
Hello,
I have a image, name image :test 3
I,map]=imread('test3','bmp');
I = ~I;
imshow(I,map);
[m n]=size(I)
P = [];
for i=1:m
for j=1:n
if I(i,j)==1
P = [P ; i j];
end
end
end
size(P)
MON=P;
[IDX,ctrs] = kmeans(MON,3)
as I plot the clusters in the image, resulting
I want to draw idx and ctrs in the image.
I don't know, How do I get back image with 3 new cluster(each cluster, different color in the image)
can anyone help ?
Thanks.
3 个评论
Salaheddin Hosseinzadeh
2014-3-13
Image Analyst should be able to help!
Image Analyst
2014-3-14
Sorry, I don't have the stats toolbox, which is what has kmeans.
Tomas
2014-3-14
采纳的回答
Dishant Arora
2014-3-13
编辑：Dishant Arora
2014-3-13
[ I map] = imread('test3.bmp');
I = ~I;
imshow(I,map);
[m n]=size(I)
P = [];
for i=1:m
for j=1:n
if I(i,j)==1
P = [P ; i j];
end
end
end
size(P)
MON=P;
[IDX,ctrs] = kmeans(MON,3);
clusterImage = zeros(size(I));
clusteredImage(sub2ind(size(I) , P(:,1) , P(:,2)))=IDX;
imshow(label2rgb(clusteredImage))
9 个评论
Tomas
2014-3-13
Dishant Arora
2014-3-14
Just some typing error, It should be:
clusteredImage = zeros(size(I))
Tomas
2014-3-14
Dishant Arora
2014-3-14
You want to show individual clusters in separate images?? if yes:
Z = cellfun(@(Z) Z', Z,'Un',0);
Z = cellfun(@(Z) cell2mat(Z), Z,'Un',0);
Z = cellfun(@(Z) sub2ind(size(I) , Z(:,1) , Z(:,2)), Z,'Un',0);
for ii = 1:length(Z)
figure
cluster{ii} = zeros(size(I));
cluster{ii}(Z{ii}) = 1;
imshow(cluster{ii})
end
Tomas
2014-3-14
Dishant Arora
2014-3-14
Z = cellfun(@(Z) Z', Z,'Un',0);
Z = cellfun(@(Z) cell2mat(Z), Z,'Un',0);
Z = cellfun(@(Z) sub2ind(size(I) , Z(:,1) , Z(:,2)), Z,'Un',0);
figure
cluster = zeros(size(I));
for ii = 1:length(Z)
cluster(Z{ii}) = ii;
end
imshow((cluster))
Tomas
2014-3-14
Dishant Arora
2014-3-15
编辑：Dishant Arora
2014-3-15
imshow(label2rgb(cluster))
Tomas
2014-3-15
更多回答（2 个）
rizwan
2015-3-16
Hi Experts, I am using the following code to find clusters in my image using K - Mean [ I map] = imread('D:\MS\Research\Classification Model\Research Implementation\EnhancedImage\ROIImage.jpeg'); I = ~I; imshow(I,map); [m n]=size(I) P = []; for i=1:m for j=1:n if I(i,j)==1 P = [P ; i j]; end end end size(P) MON=P; [IDX,ctrs] = kmeans(MON,3,'display', 'iter','MaxIter',500); clusterImage = zeros(size(I)); clusteredImage(sub2ind(size(I) , P(:,1) , P(:,2)))=IDX; imshow(label2rgb(clusteredImage))
The out put of the above code is
>> ImageEnhancement
m =
180
n =
317
ans =
20306 2
iter phase num sum
1 1 20306 9.40619e+07
2 1 2727 7.34318e+07
3 1 876 7.1216e+07
4 1 574 7.03212e+07
5 1 410 6.98473e+07
6 1 298 6.96024e+07
7 1 173 6.95038e+07
8 1 122 6.94633e+07
9 1 65 6.945e+07
10 1 45 6.9445e+07
11 1 30 6.9443e+07
12 1 15 6.94424e+07
13 1 8 6.94422e+07
14 1 3 6.94422e+07
15 1 1 6.94422e+07
16 2 0 6.94422e+07
Best total sum of distances = 6.94422e+07
Warning: Image is too big to fit on screen; displaying at 2%
Can any one explain this out put and how can i see proper out put of K- Mean???
I shall remain thank full You To
Regards
Yanyu Liang
2016-11-30
0 个投票
It shows how the kmeans is going at each iteration. "kmeans" implementation in matlab has two phases (you can think of it as two different approach to update assignment), so "phase" just tells if it is using first phase or second. "num" tells the number of points that change their assignment at that iteration (as you can see when it hits zero, the algorithm stops). "sum" is the objective value "kmeans" is trying to minimize.
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