MATLAB: draw centroids - matlab

Question

My main question is given a feature centroid, how can I draw it in MATLAB?
In more detail, I have an NxNx3 image (an RGB image) of which I take 4x4 blocks and compute a 6-dimensional feature vector for each block. I store these feature vectors in an Mx6 matrix on which I run kmeans function and obtain the centroids in a kx6 matrix, where k is the number of clusters and 6 is the number of features for each block.
How can I draw these center clusters in my image in order to visualize if the algorithm is performing the way I wish it to perform? Or if anyone has any other way/suggestions on how I can visualize the centroids on my image, I'd greatly appreciate it.

Answer 1

Here's one way you can visualize the clusters:
As you described, first I extract the blocks, compute the feature vector for each, and cluster this features matrix.
Next we can visualize the clusters assigned to each block. Note that I am assuming that the 4x4 blocks are distinct, this is important so that we can map the blocks to their location back in the original image.
Finally, in order to display the cluster centroids on the image, I simply find the closest block to each cluster and display it as a representative of that cluster.
Here's a complete example to show the above idea (in your case, you would want to replace the function that computes the features of each block by your own implementation; I am simply taking the min/max/mean/median/Q1/Q3 as my feature vector for each 4x4 block):
%# params
NUM_CLUSTERS = 3;
BLOCK_SIZE = 4;
featureFunc = #(X) [min(X); max(X); mean(X); prctile(X, [25 50 75])];
%# read image
I = imread('peppers.png');
I = double( rgb2gray(I) );
%# extract blocks as column
J = im2col(I, [BLOCK_SIZE BLOCK_SIZE], 'distinct'); %# 16-by-NumBlocks
%# compute features for each block
JJ = featureFunc(J)'; %'# NumBlocks-by-6
%# cluster blocks according to the features extracted
[clustIDX, ~, ~, Dist] = kmeans(JJ, NUM_CLUSTERS);
%# display the cluster index assigned for each block as an image
cc = reshape(clustIDX, ceil(size(I)/BLOCK_SIZE));
RGB = label2rgb(cc);
imshow(RGB), hold on
%# find and display the closest block to each cluster
[~,idx] = min(Dist);
[r c] = ind2sub(ceil(size(I)/BLOCK_SIZE), idx);
for i=1:NUM_CLUSTERS
text(c(i)+2, r(i), num2str(i), 'fontsize',20)
end
plot(c, r, 'k.', 'markersize',30)
legend('Centroids')

Answer 2

The centroids do not correspond to coordinates in the image, but to coordinates in the feature space. There is two ways you can test how well kmeans performed. For both ways, you want to fist associate the points with their closest cluster. You get this information from the first output of kmeans.
(1) You can visualize the clustering result by reducing the 6-dimensional space to 2 or 3-dimensional space and then plotting the differently classified coordinates in different colors.
Assuming that the feature vectors are collected in an array called featureArray, and that you asked for nClusters clusters, you'd do the plot as follows using mdscale to transform the data to, say, 3D space:
%# kmeans clustering
[idx,centroids6D] = kmeans(featureArray,nClusters);
%# find the dissimilarity between features in the array for mdscale.
%# Add the cluster centroids to the points, so that they get transformed by mdscale as well.
%# I assume that you use Euclidean distance.
dissimilarities = pdist([featureArray;centroids6D]);
%# transform onto 3D space
transformedCoords = mdscale(dissimilarities,3);
%# create colormap with nClusters colors
cmap = hsv(nClusters);
%# loop to plot
figure
hold on,
for c = 1:nClusters
%# plot the coordinates
currentIdx = find(idx==c);
plot3(transformedCoords(currentIdx,1),transformedCoords(currentIdx,2),...
transformedCoords(currentIdx,3),'.','Color',cmap(c,:));
%# plot the cluster centroid with a black-edged square
plot3(transformedCoords(1:end-nClusters+c,1),transformedCoords(1:end-nClusters+c,2),...
transformedCoords(1:end-nClusters+c,3),'s','MarkerFaceColor',cmap(c,:),...
MarkerEdgeColor','k');
end
(2) You can, alternatively, create a pseudo-colored image that shows you what part of the image belongs to which cluster
Assuming that you have nRows by nCols blocks, you write
%# kmeans clustering
[idx,centroids6D] = kmeans(featureArray,nClusters);
%# create image
img = reshape(idx,nRows,nCols);
%# create colormap
cmap = hsv(nClusters);
%# show the image and color according to clusters
figure
imshow(img,[])
colormap(cmap)