initial sd-vbs

initial sd-vbs

add sd-vbs

sd-vbs

1 files changed, 46 insertions, 0 deletions

diff --git a/SD-VBS/common/toolbox/toolbox_basic/textons/find_textons.m b/SD-VBS/common/toolbox/toolbox_basic/textons/find_textons.m
new file mode 100755
index 0000000..e7fa4b0
--- /dev/null
+++ b/SD-VBS/common/toolbox/toolbox_basic/textons/find_textons.m
@@ -0,0 +1,46 @@
+function [centers,label,post,d2]=find_textons(FIw,ncenters,centers_in,n_iter);
+% [centers,label,post,d2]=find_textons(FIw,ncenters,centers_in,n_iter);
+% 
+% find textons using kmeans for windowed portion FIw of filtered image 
+%
+% to start with centers pulled randomly from image, set centers_in=[]
+
+% define number of textons
+%ncenters=25;
+
+[N1,N2,N3]=size(FIw);
+% reshape filtered image stack into a long array of feature vectors
+fv=reshape(FIw,N1*N2,N3);
+% (each row is a feature vector)
+
+%centers=.01^2*randn(ncenters,N3);
+% take centers randomly from within image
+if isempty(centers_in)
+   rndnum=1+floor(N1*N2*rand(1,ncenters));
+   centers_in=fv(rndnum,:);
+end
+
+options = foptions;
+options(1)=1;           % Prints out error values.
+options(5) = 0;
+if nargin<4
+   n_iter=15;
+end
+options(14) = n_iter;           % Number of iterations.
+
+[centers,options,d2,post]=kmeans2(centers_in,fv,options);
+
+% reshuffle the centers so that the one closest to the origin
+% (featureless) comes last
+norms=sum(centers.^2,2);
+[sortval,sortind]=sort(-norms);
+centers=centers(sortind,:);
+d2=reshape(d2,N1,N2,ncenters);
+post=reshape(post,N1,N2,ncenters);
+d2=d2(:,:,sortind);
+post=post(:,:,sortind);
+
+
+% retrieve cluster number assigned to each feature vector
+[minval,label]=min(d2,[],3);
+