how run kmean algorithm on sift keypoint in matlab - matlab

Question

I need to run the K-Means algorithm on the key points of the Sift algorithm in MATLAB .I want to cluster the key points in the image but I do not know how to do it.

Answer 1

First, put the key points into X with x coordinates in the first column and y coordinates in the second column like this
X=[reshape(keypxcoord,numel(keypxcoord),1),reshape(keypycoord,numel(keypycoord),1))]
Then if you have the statistical toolbox, you can just use the built in 'kmeans' function lik this
output = kmeans(X,num_clusters)
Otherwise, write your own kmeans function:
function [ min_group, mu ] = mykmeans( X,K )
%MYKMEANS
% X = N obervations of D element vectors
% K = number of centroids
assert(K > 0);
D = size(X,1); %No. of r.v.
N = size(X,2); %No. of observations
group_size = zeros(1,K);
min_group = zeros(1,N);
step = 0;
%% init centroids
mu = kpp(X,K);
%% 2-phase iterative approach (local then global)
while step < 400
%% phase 1, batch update
old_group = min_group;
% computing distances
d2 = distances2(X,mu);
% reassignment all points to closest centroids
[~, min_group] = min(d2,[],1);
% recomputing centroids (K number of means)
for k = 1 : K
group_size(k) = sum(min_group==k);
% check empty group
%if group_size(k) == 0
assert(group_size(k)>0);
%else
mu(:,k) = sum(X(:,min_group==k),2)/group_size(k);
%end
end
changed = sum(min_group ~= old_group);
p1_converged = changed <= N*0.001;
%% phase 2, individual update
changed = 0;
for n = 1 : N
d2 = distances2(X(:,n),mu);
[~, new_group] = min(d2,[],1);
% recomputing centroids of affected groups
k = min_group(n);
if (new_group ~= k)
mu(:,k)=(mu(:,k)*group_size(k)-X(:,n));
group_size(k) = group_size(k) - 1;
mu(:,k)=mu(:,k)/group_size(k);
mu(:,new_group) = mu(:,new_group)*group_size(new_group)+ X(:,n);
group_size(new_group) = group_size(new_group) + 1;
mu(:,new_group)=mu(:,new_group)/group_size(new_group);
min_group(n) = new_group;
changed = changed + 1;
end
end
%% check convergence
if p1_converged && changed <= N*0.001
break;
else
step = step + 1;
end
end
end
function d2 = distances2(X, mu)
K = size(mu,2);
N = size(X,2);
d2 = zeros(K,N);
for j = 1 : K
d2(j,:) = sum((X - repmat(mu(:,j),1,N)).^2,1);
end
end
function mu = kpp( X,K )
% kmeans++ init
D = size(X,1); %No. of r.v.
N = size(X,2); %No. of observations
mu = zeros(D, K);
mu(:,1) = X(:,round(rand(1) * (size(X, 2)-1)+1));
for k = 2 : K
% computing distances between centroids and observations
d2 = distances2(X, mu(1:k-1));
% assignment
[min_dist, ~] = min(d2,[],1);
% select new centroids by selecting point with the cumulative dist
% value (distance) larger than random value (falls in range between
% dist(n-1) : dist(n), dist(0)= 0)
rv = sum(min_dist) * rand(1);
for n = 1 : N
if min_dist(n) >= rv
mu(:,k) = X(:,n);
break;
else
rv = rv - min_dist(n);
end
end
end
end