image segmentation

Question

pdp rpa 2012-3-24

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/33312-image-segmentation

Sir i have been trying the following code for image segmentation using dbscan.

But the problem is that this code is runnung infinite time.I am not getting the solution and it has taken away my sleep. plz sir i really need your help.

This is my project work and the deadline is also very near

he = imread('test.jpg');
ab = double(he(:,:,2:3));
nrows = size(ab,1);
ncols = size(ab,2);
X = reshape(ab,nrows*ncols,2);
K=6;
[class,type]=dbscannew(X,K)
%The m file for the function dbscannew is given below : dbscannew.m
function [class,type]=dbscannew(X,K,d)
% [class,type]=dbscan(X,K,d)
% X: matrix
% K: minimal points
% d: maximal distance
% class: assignments
% type: core: 1, border: 0, outlier: -1
if nargin<3
     d=epsilon(X,K);
  end
  m=size(X, 1);
  class=zeros(m,1);
  type=zeros(m,1);
  touched=zeros(m,1);
  c=1;
  for i=1:m
      if touched(i) continue; end
      indices=find(distances(X(i,:),X)<=d);
      k=length(indices);
      if k==1
         type(i)=-1;
         class(i)=-1;  
         touched(i)=1;
      elseif k<=K
         type(i)=0;
         class(i)=0;
      else
         type(i)=1;
         class(indices)=c;
         while ~isempty(indices)
             index=indices(1);
             touched(index)=1;
             I=find(distances(X(index,:),X)<=d);
             if length(I)>1
                 class(I)=c;
                 if length(I)<=K;
                    type(index)=0;
                 else
                    type(index)=1;
                 end
                 for i=1:length(I)
                     if touched(I(i)) continue; end
                     touched(I(i))=1;
                     indices(end+1)=I(i);
                     class(I(i))=c;
                 end
             end
             indices=indices(2:end);
         end
         c=c+1;
      end
  end
  I=find(class==0);
  class(I)=-1;
  type(I)=-1;
   function d=epsilon(x,k)
  [m,n]=size(x);
  d=((prod(max(x)-min(x))*k*gamma(.5*n+1))/(m*sqrt(pi.^n))).^(1/n);
   function D=distances(x,X)
  D=sqrt(sum((ones(size(X, 1),1)*x-X).^2, 2));