k means clustering, error
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im = imread('hestain.png');
im=rgb2gray(im) //if you only want grayscale intensities
[idx centroids]=kmeans(double(im(:)),4);
??? Error using ==> kmeans>batchUpdate at 436
Empty cluster created at iteration 1.
Error in ==> kmeans at 337
converged = batchUpdate();
Error in ==> Untitled2 at 5
[idx centroids]=kmeans(double(im(:)),4);
Does anyone help ?
Thanks.
回答（2 个）
Walter Roberson
2014-3-15
0 个投票
Look at the documentation page, at the 'emptyaction' option.
Image Analyst
2014-3-15
0 个投票
Evidently you've adapted the demo at this web page to your image incorrectly. You don't have clusters because you don't have a second axis. You just passed in a 1D vector. What is the other axis that you think you have that forms clusters?
6 个评论
Tomas
2014-3-15
Image Analyst
2014-3-15
That does not make sense. You need to study up some more on what kmeans means.
Tomas
2014-3-15
I understand k means method. I don't understand image proccesing and computer vision.
give examples about what I need
in binary images
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)
clusteredImage = zeros(size(I));
clusteredImage(sub2ind(size(I) , P(:,1) , P(:,2)))=IDX;
imshow(label2rgb(clusteredImage))
my output:
I need this also with grayscale and RGB image.
Image Analyst
2014-3-15
You can do simple color classification by simple thresholding in RGB color space with that image. No kmeans clustering is needed. See my rgb color classification demo in my File Exchange: http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A31862 For example if you want a map (binary image) of where all the pure blue, cyan, or yellow pixels are, you simply do this:
% Extract the individual red, green, and blue color channels.
redChannel = rgbImage(:, :, 1);
greenChannel = rgbImage(:, :, 2);
blueChannel = rgbImage(:, :, 3);
pureYellowPixels = redChannel == 255 & greenChannel == 255 & blueChannel == 0;
pureCyanPixels = redChannel == 0 & greenChannel == 255 & blueChannel == 255;
pureBluePixels = redChannel == 0 & greenChannel == 0 & blueChannel == 255;
If you want a N by 2 listing of all the (row, column) pairs where the pixels lie, just do this:
[rowsY, columnsY] = find(pureYellowPixels);
[rowsC, columnsC] = find(pureCyanPixels);
[rowsB, columnsB] = find(pureBluePixels);
But that kind of thing is rarely needed.
Tomas
2014-3-15
Image Analyst
2014-3-16
Then you must know more about it than me. So again I ask you what are the two measures, what are the two axes that something is going to be plotted against, if you're not going to use the locations (rows, columns) like I derived for you? And if you do use those, then there's no need to classify because the classification was done first in order to get the coordinates.
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2014-3-16
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