i am getting an error in this code,like memory exceeded for variable usage.can you help me.or is there any other code for normalised cut segmentation algorithm.

% % % Grayscale image segmentation using normalized graph cuts (Shi & Malik 2000)

% input is a grayscale image 'im' % outputs are the subgraphs % This code is the stock code for graph cut segmentation % This code can be modified for user's application by choosing different % splitting points and adding constraints like minsize of the graph, % sigmaI, sigmaX values

% % for bi-partitions uncomment lines (105-235) and run % % for k simultaneous partitions uncomment lines (240-265) and run

clear all t0=cputime; im = imread('lenna.pgm');

% resizing to avoid out of memory error cim=imresize(im,[100 100]); [r, c]=size(cim); ind=find(cim); lind=length(ind); [I,J]=ind2sub([r,c],ind);

% % I've used linear indexing to speed up the partitioning

% vectoring the pixel nodes for i=1:lind V1(i)=double(cim(ind(i))); end

% normalizing to [0-1] scale V=(V1./255);

% w is the weight matrix (similarity matrix or adjacency matrix) w=zeros(lind,lind);

% r, sigmaI, sigmaX values rad=5; sigi=.1; sigx=.3;

% computing the weight matrix for i=1:lind x1=I(i,1); y1=J(i,1);

    for j=1:lind
         if (i==j)
                 w(i,j)=1;
         else

                 x2=I(j,1);
                 y2=J(j,1);

            dist=((x1-x2)^2 + (y1-y2)^2);  
            if sqrt(dist)>=rad
                dx=0;            
            else
                dx=exp(-((dist)/(sigx^2)));
            end

            pdiff=(V(i)-V(j))^2;
            di=exp(-((pdiff)/(sigi)^2));  
            w(i,j)=di*dx;
        end

    end
end

d=zeros(lind,lind); s=sum(w,2);

% the diagonal matrix for computing the laplacian matrix for i=1:lind d(i,i)=s(i); end

A=zeros(lind,lind); A=(d-w); % A is the laplacian matrix

% vt has the eigen vectors corresponding to eigen values in vl % other eigs / eig functions in matlab can be used but I'm using the % function to compute the 5 smallest eigenvectors [vt,vl]=eigs(A,d,5,'sm');

% % % Bi - Partitions

% fist binary partition

% se has the second smallest eigen vector, third and so on se=vt(:,2:4); id(:,:)=se; id(:,4)=ind(1:length(ind)); idk=id;

% % med1 is the first splitting point, can be 0 or mean or median % % the mean as the splitting point has the minimum Ncut value % % % % med1=median(se(:,1)); % med1=0; % % med1=mean(se(:,1)); % g1=find(idk(:,1)>=med1); % gp1=idk(g1,:); % imp1=zeros(r,c); % imp1(gp1(:,4))=cim(gp1(:,4)); % % figure,imshow(uint8(imp1)); % % g2=find(idk(:,1)<med1); % gp2=idk(g2,:); % imp2=zeros(r,c); % imp2(gp2(:,4))=cim(gp2(:,4)); % % figure,imshow(uint8(imp2)); % % % % % % % % % % Second level partitioning (4 subgraphs as the result ) % % % med2 and med3 are the splitting points for second bipartitioning % % med2=0; % med2=median(gp1(:,2)); % % med2=mean(gp1(:,2)); % g11=find(gp1(:,2)>med2); % gp11=gp1(g11,:); % imp11=zeros(r,c); % imp11(gp11(:,4))=cim(gp11(:,4)); % % figure,imshow(uint8(imp11)); % % g12=find(gp1(:,2)<=med2); % gp12=gp1(g12,:); % imp12=zeros(r,c); % imp12(gp12(:,4))=cim(gp12(:,4)); % % figure,imshow(uint8(imp12)); % % % % med3=0; % med3=median(gp2(:,2)); % % med3=mean(gp2(:,2)); % g21=find(gp2(:,2)>med3); % gp21=gp2(g21,:); % imp21=zeros(r,c); % imp21(gp21(:,4))=cim(gp21(:,4)); % % figure,imshow(uint8(imp21)); % % g22=find(gp2(:,2)<=med3); % gp22=gp2(g22,:); % imp22=zeros(r,c); % imp22(gp22(:,4))=cim(gp22(:,4)); % % figure,imshow(uint8(imp22));

% % % % % third level bipartioning resulting in 8 subgraphs % % med4, med5, med6, med7 are the corresponing splitting points % % % more partitions % % % % % med4=0; % % % med4=median(gp11(:,3)); % % % g111=find(gp11(:,3)>med4); % % % gp111=gp11(g111,:); % % % imp111=zeros(r,c); % % % imp111(gp111(:,4))=cim(gp111(:,4)); % % % % figure,imshow(uint8(imp111)); % % % % % % g112=find(gp11(:,3)<=med4); % % % gp112=gp11(g112,:); % % % imp112=zeros(r,c); % % % imp112(gp112(:,4))=cim(gp112(:,4)); % % % % figure,imshow(uint8(imp112)); % % % % % med5=0; % % % med5=median(gp12(:,3)); % % % g121=find(gp12(:,3)>med5); % % % gp121=gp12(g121,:); % % % imp121=zeros(r,c); % % % imp121(gp121(:,4))=cim(gp121(:,4)); % % % figure,imshow(uint8(imp121)); % % % % % % g122=find(gp12(:,3)<=med5); % % % gp122=gp12(g122,:); % % % imp122=zeros(r,c); % % % imp122(gp122(:,4))=cim(gp122(:,4)); % % % figure,imshow(uint8(imp122)); % % % % % med6=0; % % % med6=median(gp21(:,3)); % % % g211=find(gp21(:,3)>med6); % % % gp211=gp21(g211,:); % % % imp211=zeros(r,c); % % % imp211(gp211(:,4))=cim(gp211(:,4)); % % % % figure,imshow(uint8(imp211)); % % % % % % g212=find(gp21(:,3)<=med6); % % % gp212=gp21(g212,:); % % % imp212=zeros(r,c); % % % imp212(gp212(:,4))=cim(gp212(:,4)); % % % % figure,imshow(uint8(imp212)); % % % % % % % med7=0; % % % med7=median(gp22(:,3)); % % % g221=find(gp22(:,3)>med7); % % % gp221=gp22(g221,:); % % % imp221=zeros(r,c); % % % imp221(gp221(:,4))=cim(gp221(:,4)); % % % % figure,imshow(uint8(imp221)); % % % % % % g222=find(gp22(:,3)<=med7); % % % gp222=gp22(g222,:); % % % imp222=zeros(r,c); % % % imp222(gp222(:,4))=cim(gp222(:,4)); % % % % figure,imshow(uint8(imp222));

% figure; % subplot(2,4,1);imshow(uint8(im));title('Original image'); % subplot(2,4,2);imshow(uint8(cim));title('Preprocessed image'); % subplot(2,4,3);imshow(uint8(imp1));title('Partition 1'); % subplot(2,4,4);imshow(uint8(imp2));title('Partition 2'); % subplot(2,4,5);imshow(uint8(imp11));title('Partition 1-1'); % subplot(2,4,6);imshow(uint8(imp12));title('Partition 1-2'); % subplot(2,4,7);imshow(uint8(imp21));title('Partition 2-1'); % subplot(2,4,8);imshow(uint8(imp22));title('Partition 2-2');

% % % % % Simultaneous 'k' partitions

% k=6; % id=kmeans(se,k); % imp=cell(1,k); % pic=cell(1,k); % % for i=1:k % imp{1,i}= find(id(:,1)==i); % mat=zeros(100,100); % in=imp{1,i}; % mat(in)=cim(in); % pic{1,i}=uint8(mat); % % figure,imshow(pic{1,i}); % end % % pic has the sub graphs or partitiond sub images % % figure; % subplot(2,4,1);imshow(uint8(im));title('Original image'); % subplot(2,4,2);imshow(uint8(cim));title('Preprocessed image'); % subplot(2,4,3);imshow(pic{1,1});title('Partition 1'); % subplot(2,4,4);imshow(pic{1,2});title('Partition 2'); % subplot(2,4,5);imshow(pic{1,3});title('Partition 3'); % subplot(2,4,6);imshow(pic{1,4});title('Partition 4'); % subplot(2,4,7);imshow(pic{1,5});title('Partition 5'); % subplot(2,4,8);imshow(pic{1,6});title('Partition 6');

t1=cputime-t0;