K means clustering in matlab - algorithm

Question

I found the below code to segment the images using K means clustering,but in the below code,they are using some calculation to find the min,max values.I know the basic concept of K-means algorithm.but I couldn't understand this code.Can any one please explain.
function [Centroid,new_cluster]=kmeans_algorithm(input_image,k)
% k = 4;
input_image=double(input_image);
new_image=input_image;
input_image=input_image(:);
min_val=min(input_image);
input_image=round(input_image-min_val+1);
length_input_image=length(input_image);
max_val=max(input_image)+1;
hist_gram=zeros(1,max_val);
hist_gram_count=zeros(1,max_val);
for i=1:length_input_image
if(input_image(i)>0)
hist_gram(input_image(i))=hist_gram(input_image(i))+1;
end;
end
IDX=find(hist_gram);
hist_length=length(IDX);
Centroid=(1:k)*max_val/(k+1);
while(true)
old_Centroid=Centroid;
for i=1:hist_length
new_val=abs(IDX(i)-Centroid);
hist_val=find(new_val==min(new_val));
hist_gram_count(IDX(i))=hist_val(1);
end
for i=1:k,
loop_count=find(hist_gram_count==i);
Centroid(i)=sum(loop_count.*hist_gram(loop_count))/sum(hist_gram(loop_count));
end
if(Centroid==old_Centroid) break;end;
end
length_input_image=size(new_image);
new_cluster=zeros(length_input_image);
for i=1:length_input_image(1),
for j=1:length_input_image(2),
new_val=abs(new_image(i,j)-Centroid);
loop_count=find(new_val==min(new_val));
new_cluster(i,j)=loop_count(1);
end
end
Centroid=Centroid+min_val-1;
especially what's the purpose of this input_image(:)in the above code. In google they said it like matrix.but still I'm confused,whether this is matrix or array

Answer 1

The notation (:) collapses a multi-dimensional vector into a column vector.
data = rand(10,4);
size(data(:))
% 40 1
Then you can apply a normal function to an entire multi-dimensional array
min(data(:));
Instead of to each dimension independently
min(min(data));
In the code that you have posted, they collapse input_image to a column vector just to make it easier to apply functions like min, max, and length.
Update
The code that you have posted doesn't actually perform k-means clustering. It simply creates a histogram of all values in the image. They use min and max to determine the number of bins to use for the histogram.