Image segmentation - part 2

This continues the previous post where we talked about segmenting a test image into 3 parts. As a reminder, here's the test image again.

k-means clustering takes your data points and tries to group them into k clusters. It's a general technique that can be used with many sorts of data but it can be useful for segmenting images.

If you want to segment by color, it's often easier to switch to the L*a*b color-space. When you do this, the second and third planes contain all of the color data. The first L plane is to do with brightness.

Here's some MATLAB code that

1. loads our test image
2. transforms it into the lab color space
3. calculates the 2D histogram of pixel intensities
4. displays the heat map of the histogram with a log color scale

function k_means_clustering

image_file_string = 'hubert_8_3_small.png';

% Load image

im_rgb = imread(image_file_string);

% Transform to lab color space

cform = makecform('srgb2lab');

im_lab = applycform(im_rgb,cform);

% Create a n x 2 matrix of [a b] values

a = im_lab(:,:,2);

a = double(a(:));

b = im_lab(:,:,3);

b = double(b(:));

ab = [a b];

% Construct the 2D histogram with appropriate limits

c{1}=[min(ab(:)) : max(ab(:))];

c{2}=c{1};

n = hist3(ab,c);

n1 = n';

n1(size(n,1) + 1, size(n,2) + 1) = 0;

% Display

figure(1);

clf;

imagesc(log10(n1));

hold on;

title('2D histogram of pixel values, colored based on log10 of pixel count')

xlabel('a value');

ylabel('b value');

The output is

When I look at the test image, I see three colors: a gray/white that forms the background, an orangey-yellow that is muscle tissue, and a darker red that indicates collagen. So let's try to split the pixel values into 3 separate groups. In MATLAB, the command is

[id,cl]=kmeans(ab,3);

id is a vector with values of 1,2, or 3. These link each pixel to a given cluster.
cl is a 3x2 array which shows the centroids of the clusters.

This snippet of code shows which [a,b] values fall into each cluster. The magenta markers show the centroids (cl) of each cluster.

figure(2);
clf;
hold on;
cm = paruly(3);
for i=1:3
    vi = find(id==i);
    plot(ab(vi,1),ab(vi,2),'+','Color',cm(i,:));
    plot(cl(i,1),cl(i,2),'md','MarkerFaceColor','m');
end

set(gca,'YDir','reverse');

giving

It's now easy to set each pixel in the original image based on it's cluster identity. You just have to reshape the id vector.

[r,c] = size(im_rgb(:,:,1));
im_cluster = reshape(id,r,c);

figure(3);
imagesc(im_cluster);

The output is

Compare this result to the original.

The segmentation is not perfect but we didn't set any parameters other than 'we want 3 colors'. It's also super-fast and objective. I think it's pretty cool.

Is k means clustering a perfect solution for image segmentation? We will look at that in the next post.