Efficient solution to object detection algorithm - matlab

Question

I'm trying to implement this paper 'Salient Object detection by composition' here is the link: http://research.microsoft.com/en-us/people/yichenw/iccv11_salientobjectdetection.pdf
I have implemented the algorithm but it takes a long time to execute and display the output. I'm using 4 for loops in the code(Using for loops is the only way I could think of to implement this algorithm.) I have searched online for MATLAB code, but couldn't find anything. So can anyone please suggest any faster way to implement the algorithm. Also in the paper they(the authors) say that they have implemented the code using MATLAB and it runs quickly. So there definitely is a way to write the code more efficiently.
I appreciate any hint or code to execute this algorithm efficiently.
clc
clear all
close all
%%instructions to run segment.cpp
%to run this code
%we need an output image
%segment sigma K min input output
%sigma: used for gaussian smoothing of the image
%K: scale of observation; larger K means larger components in segmentation
%min: minimum component size enforced by post processing
%%
%calculating composition cost for each segment
I_org = imread('segment\1.ppm');
I = imread('segment\output1.ppm');
[rows,cols,dims] = size(I);
pixels = zeros(rows*cols,dims);
red_channel = I(:,:,1);
green_channel = I(:,:,2);
blue_channel = I(:,:,3);
[unique_pixels,count_pixels] = countPixels(I);
no_segments = size(count_pixels,1);
area_segments = count_pixels ./ (rows * cols);
appearance_distance = zeros(no_segments,no_segments);
spatial_distance = zeros(no_segments,no_segments);
thresh = multithresh(I_org,11);
thresh_values = [0 thresh];
for i = 1:no_segments
leave_pixel = unique_pixels(i,:);
mask_image = ((I(:,:,1) == leave_pixel(1)) & (I(:,:,2) == leave_pixel(2)) & (I(:,:,3) == leave_pixel(3)));
I_i(:,:,1) = I_org(:,:,1) .* uint8((mask_image));
I_i(:,:,2) = I_org(:,:,2) .* uint8((mask_image));
I_i(:,:,3) = I_org(:,:,3) .* uint8((mask_image));
LAB_trans = makecform('srgb2lab');
I_i_LAB = applycform(I_i,LAB_trans);
L_i_LAB = imhist(I_i_LAB(:,:,1));
A_i_LAB = imhist(I_i_LAB(:,:,2));
B_i_LAB = imhist(I_i_LAB(:,:,3));
for j = i:no_segments
leave_pixel = unique_pixels(j,:);
mask_image = ((I(:,:,1) == leave_pixel(1)) & (I(:,:,2) == leave_pixel(2)) & (I(:,:,3) == leave_pixel(3)));
I_j(:,:,1) = I_org(:,:,1) .* uint8((mask_image));
I_j(:,:,2) = I_org(:,:,2) .* uint8((mask_image));
I_j(:,:,3) = I_org(:,:,3) .* uint8((mask_image));
I_j_LAB = applycform(I_j,LAB_trans);
L_j_LAB = imhist(I_j_LAB(:,:,1));
A_j_LAB = imhist(I_j_LAB(:,:,2));
B_j_LAB = imhist(I_j_LAB(:,:,3));
appearance_distance(i,j) = sum(min(L_i_LAB,L_j_LAB) + min(A_i_LAB,A_j_LAB) + min(B_i_LAB,B_j_LAB));
spatial_distance(i,j) = ModHausdorffDist(I_i,I_j) / max(rows,cols);
end
end
spatial_distance = spatial_distance ./ max(max(spatial_distance));
max_apperance_distance = max(max(appearance_distance));
composition_cost = ((1 - spatial_distance) .* appearance_distance) + (spatial_distance * max_apperance_distance);
%%
%input parameters for computation
window_size = 9; %rows and colums are considered to be same
window = ones(window_size);
additional_elements = (window_size - 1)/2;
I_temp(:,:,1) = [zeros(additional_elements,cols);I(:,:,1);zeros(additional_elements,cols)];
I_new(:,:,1) = [zeros(rows + (window_size - 1),additional_elements) I_temp(:,:,1) zeros(rows + (window_size - 1),additional_elements)];
I_temp(:,:,2) = [zeros(additional_elements,cols);I(:,:,2);zeros(additional_elements,cols)];
I_new(:,:,2) = [zeros(rows + (window_size - 1),additional_elements) I_temp(:,:,2) zeros(rows + (window_size - 1),additional_elements)];
I_temp(:,:,3) = [zeros(additional_elements,cols);I(:,:,3);zeros(additional_elements,cols)];
I_new(:,:,3) = [zeros(rows + (window_size - 1),additional_elements) I_temp(:,:,3) zeros(rows + (window_size - 1),additional_elements)];
cost = zeros(rows,cols);
for i = additional_elements + 1:rows
for j = additional_elements+1:cols
I_windowed(:,:,1) = I_new(i-additional_elements:i+additional_elements,i-additional_elements:i+additional_elements,1);
I_windowed(:,:,2) = I_new(i-additional_elements:i+additional_elements,i-additional_elements:i+additional_elements,2);
I_windowed(:,:,3) = I_new(i-additional_elements:i+additional_elements,i-additional_elements:i+additional_elements,3);
[unique_pixels_w,count_pixels_w] = countPixels(I_windowed);
unique_pixels_w = setdiff(unique_pixels_w,[0 0 0],'rows');
inside_segment = setdiff(unique_pixels,unique_pixels_w);
outside_segments = setdiff(unique_pixels,inside_segment);
area_segment = count_pixels_w;
for k = 1:size(inside_pixels,1)
current_segment = inside_segment(k,:);
cost_curr_seg = sort(composition_cost(ismember(unique_pixels,current_segment,'rows'),:));
for l = 1:size(cost_curr_seg,2)
if(ismember(unique_pixels(l,:),outside_segments,'rows') && count_pixels(l) > 0)
composed_area = min(area_segment(k),count_pixels(l));
cost(i,j) = cost(i,j) + cost_curr_seg(l) * composed_area;
area_segment(k) = area_segment(k) - composed_area;
count_pixels(l) = count_pixels(l) - composed_area;
if area_segment(k) == 0
break
end
end
end
if area(k) > 0
cost(i,j) = cost(i,j) + max_apperance_distance * area_segment(k);
end
end
end
end
cost = cost / window_size;
The code for the countPixels function:
function [unique_rows,counts] = countPixels(I)
[rows,cols,dims] = size(I);
pixels_I = zeros(rows*cols,dims);
count = 1;
for i = 1:rows
for j = 1:cols
pixels_I(count,:) = reshape(I(i,j,:),[1,3]);
count = count + 1;
end
end
[unique_rows,~,ind] = unique(pixels_I,'rows');
counts = histc(ind,unique(ind));
end