www.pudn.com > Ms_segmenter.rar > Color_segment_svd2.m

function [New_image, Modes] = ... 
   Color_segment_svd2 (Luv_image, Wind, 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 = 0.005*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 and second columns of U only 
U_col = U(:, 1:2); 
U_col(:, 2) = U_col(:, 2)*S(2, 2)/S(1, 1);		% scaling it 
Max = max(max(U_col)); 
Min = min(min(U_col)); 
Max_axis = 255; 
U_new = round((U_col-Min*ones(size(U_col)))/(Max-Min)*Max_axis)'; 
 
% creating a new matrix - color based 
Cumulative = sparse(Max_axis+1, Max_axis+1); 
for i=1:size(U_new, 2) 
	Cumulative(U_new(1, i)+1, U_new(2, i)+1) = ... 
		Cumulative(U_new(1, i)+1, U_new(2, i)+1) + 1; 
end 
 
% for background detection only 
Cumulative(1, 1) = 0; 
 
k = 0; L = Max_axis+1;  Max_iterats = 1000; 
while sum(sum(Cumulative)) > Min_image_size 
   Non_zero_pos = find(Cumulative > 0); 
   if length(Non_zero_pos) == 0 
      break; 
   end 
    
	[Init(1), Init(2)] = ind2sub(size(Cumulative), Non_zero_pos(1)); 
    
   [Mode, Number_values] = M_shift2(Cumulative, Wind, Init, Min_group_size/5); 
   Mode = full(round(Mode)); 
   if Number_values > Min_group_size	% good group 
		k = k+1; 
      Modes(:, k) = Mode'; 
		Cumulative(... 
         max(1, Mode(1)-Wind):min(L, Mode(1)+Wind), ... 
         max(1, Mode(2)-Wind):min(L, Mode(2)+Wind)) = ... 
         zeros(size(Cumulative(... 
         max(1, Mode(1)-Wind):min(L, Mode(1)+Wind), ... 
         max(1, Mode(2)-Wind):min(L, Mode(2)+Wind)))); 
   else 
		Cumulative(... 
         max(1, Mode(1)-Wind):min(L, Mode(1)+Wind), ... 
         max(1, Mode(2)-Wind):min(L, Mode(2)+Wind)) = ... 
         zeros(size(Cumulative(... 
         max(1, Mode(1)-Wind):min(L, Mode(1)+Wind), ... 
         max(1, Mode(2)-Wind):min(L, Mode(2)+Wind)))); 
   end 
	Max_iterats = Max_iterats-1; 
	if Max_iterats < 0 
		break; 
	end 
end 
 
Ones = ones(1, size(Luv_vect, 2)); 
New_image = zeros(1, size(Luv_vect, 2)); 
for i=1:size(Modes, 2) 
   Center = Modes(:, i) * Ones; 
   Norms = max(abs(Center - U_new)); 
   To_take = find(Norms <= Wind); 
   New_image(To_take) = i; 
end 
% detecting background 
To_zero = find(Luv_vect(1, :) == 0); 
New_image(To_zero) = 0; 
New_image = reshape(New_image, [size(Luv_image, 1), size(Luv_image, 2)]);