问题
To perform K means clustering with k = 3 (segments). So I:
1) Converted the RGB img into grayscale
2) Casted the original image into a n X 1, column matrix
3) idx = kmeans(column_matrix)
4) output = idx, casted back into the same dimensions as the original image.
My questions are :
A
When I do imshow(output), I get a plain white image. However when I do imshow(output[0 5]), it shows the output image. I understand that 0 and 5 specify the display range. But why do I have to do this?
B) Now the output image is meant to be split into 3 segments right. How do I threshold it such that I assign a
0 for the clusters of region 1 1 for clusters of region 2 2 for clusters of region 3
As the whole point of me doing this clustering is so that I can segment the image into 3 regions.
Many thanks.
Kind Regards.
回答1:
A: Given that your matrix output contains scalar values ranging from 1 to 3, imshow(output) is treating this as a grayscale matrix and assuming that the full range of values is 0 to 255. This is why constraining the color limits is necessary as otherwise your image is all white or almost all white.
B: output = output - 1
回答2:
As pointed out by Ryan, your problem is probably just how you display the image. Here's a working example:
snow = rand(256, 256);
figure;
imagesc(snow);
nClusters = 3;
clusterIndices = kmeans(snow(:), nClusters);
figure;
imagesc(reshape(clusterIndices, [256, 256]));
来源：https://stackoverflow.com/questions/16690669/k-means-clustering-in-matlab-output-image