www.pudn.com > Ms_segmenter.rar > Color_segment_svd1.m

function [New_image, Sorted_modes] = ... 
   Color_segment_svd (Luv_image, Window_radius, Min_group_size) 
% Now first tries to find "the most valuable components" of the image by 
% using svd decomposition, which gives 3 matrices - U, having the same number 
% of rows as the original vector, and 2 matrices - diagonal which indicates 
% weights of the columns, and V which is disregarded at the moment.  Then the 
% original meanshift algorithm is applied to either first or two first columns 
% of U, both done sufficiently quickly and more or less reliably 
 
% Min_group_size : need at least that many pixels to qualify for a color 
Rows = size(Luv_image, 1); 
Cols = size(Luv_image, 2); 
Min_image_size = 0.05*Rows*Cols;		% to stop when image is small 
 
Luv_vect = reshape(shiftdim(Luv_image, 2), [3, Rows*Cols]); 
[U, S, V] = svd(double(Luv_vect'), 0); 
 
% now segmenting based on the first column of U only 
U_col = U(:, 1);  
Max = max(U_col); 
Min = min(U_col); 
U_new = round((U_col-Min*ones(size(U_col)))/(Max-Min)*255); 
Gray_image = reshape(U_new, [Rows, Cols]); 
 
% creating a new array - color based 
Cumulative = sparse(1, max(max(Gray_image))+1); 
for i=1:Rows 
   for j=1:Cols 
      Cumulative(Gray_image(i, j)+1) = Cumulative(Gray_image(i, j)+1)+1; 
   end 
end 
 
Cumulative(1) = 0;		% corresponds to only manually zeroed out regions 
k = 0; L = length(Cumulative); 
while sum(Cumulative) > Min_image_size 
   Non_zero_pos = find(Cumulative > 0); 
   if length(Non_zero_pos) == 0 
      break; 
   end 
    
   Initial = Non_zero_pos(1); 
   [Mode, Number_values] = M_shift1(Cumulative, Window_radius, Initial); 
   Mode = full(round(Mode)); 
    
   if Number_values > Min_group_size	% good group 
		k = k+1; 
      Modes(k) = Mode; 
      Cumulative(max(1, Mode-Window_radius):min(L, Mode+Window_radius)) = ... 
      zeros(size(Cumulative(max(1, Mode-Window_radius): ... 
      min(L, Mode+Window_radius)))); 
   else 
     	Cumulative(max(1, Initial-Window_radius):min(L, Initial+Window_radius))... 
			= zeros(size(Cumulative(max(1, Initial-Window_radius): ... 
      	min(L, Initial+Window_radius)))); 
   end 
end 
 
Sorted_modes = sort(Modes); 
New_image = zeros(size(Gray_image)); 
for i=1:k 
	To_group = find((Gray_image >= Sorted_modes(i)-Window_radius)... 
		& (Gray_image < Sorted_modes(i)+Window_radius)); 
	New_image(To_group) = i; 
end 
% Gray_image == 0 added to provide for background detection 
To_zero = find(Gray_image == 0); 
New_image(To_zero) = 0;