Demo step by step:
Image Segmentation with Normalized Cuts
(file demoNcutImage.m)
%% read
image, change color image to brightness image, resize to 160x160
I = imread_ncut('specific_NcutImage_files/jpg_images/3.jpg',160,160);
%% display the image
figure(1);clf; imagesc(I);colormap(gray);axis off;
disp('This is the input image to segment, press Enter to continue...');
pause;
This is the input image to
segment, press Enter to continue...
%%
compute the edges imageEdges, the similarity matrix W based on
%% Intervening Contours, the Ncut eigenvectors and discrete segmentation
nbSegments = 5;
disp('computing Ncut eigenvectors ...');
tic;
[SegLabel,NcutDiscrete,NcutEigenvectors,NcutEigenvalues,W,imageEdges]=
NcutImage(I,nbSegments);
disp(['The computation took ' num2str(toc) ' seconds on the '
num2str(size(I,1)) 'x' num2str(size(I,2)) ' image']);
computing Ncut eigenvectors
...
The computation took 14.5 seconds on the 160x160 image
%% display the edges
figure(2);clf; imagesc(imageEdges); axis off
disp('This is the edges computed, press Enter to continue...');
pause;
This is the edges computed,
press Enter to continue...
%% display
the segmentation
figure(3);clf
bw = edge(SegLabel,0.01);
J1=showmask(I,imdilate(bw,ones(2,2))); imagesc(J1);axis off
disp('This is the segmentation, press Enter to continue...');
pause;
This is the
segmentation, press Enter to continue...
%% display
Ncut eigenvectors
figure(4);clf;set(gcf,'Position',[100,500,200*(nbSegments+1),200]);
[nr,nc,nb] = size(I);
for i=1:nbSegments
subplot(1,nbSegments,i);
imagesc(reshape(NcutEigenvectors(:,i) ,
nr,nc));axis('image');axis off;
end
disp('This is the Ncut eigenvectors...');
disp('The demo is finished.');
This is the clustering result
The demo is finished.