rrsaliency.m

function saloutput = rrsaliency(IMG_DIR, imgnamein)
% Function saloutput = rrsaliency(IMG_DIR, imgnamein) executes the 
% proposed regularized random walk ranking algorithm and output the saliency map
% result. See demo_rrwr.m for an example of usage.
% 
% Inputs:       IMG_DIR - path of image input image
%                   imgnamein - name of the input image (without path and
%                   suffix); only *.jpg files are supported
% Outputs:    saloutput - the pixel-wise saliency map with the same size as
%                   the input image
% 
% 11/05/14 - by Yuchen Yuan
% Based on the paper:
% Changyang Li, Yuchen Yuan, Weidong Cai, Yong Xia, and David Dagan Feng. 
% "Robust Saliency Detection via Regularized Random Walks Ranking". CVPR 2015

% Parameter initialization    
    spn = 200; 
    itheta = 10;
    alpha = 0.99;
    
% Step 1 & 2: Saliency Estimation
    imgname = [IMG_DIR, imgnamein(1:end-4) '.jpg'];
    imgbmpname = strcat(imgname(1:(end-4)), '.bmp');
    [img, wid] = removeframe(imgname);
    img = uint8(img*255);
    [m, n, ~] = size(img);
    comm = ['SLIC_SUPPORT' ' ' imgbmpname ' ' int2str(20) ' ' int2str(spn) ' '];
    evalc('system(comm)');
    spname = [imgbmpname(1:end-4)  '.dat'];
    superpixels = ReadDAT([m,n], spname);
    spno = max(superpixels(:));
    delete(imgbmpname);
    delete(spname);
    delete([spname(1:end-4) '_SLIC.bmp']);
	salest = salestimation(img, superpixels, spno, itheta, alpha);  

% Step 3: regularized random walk ranking
	th1 = (mean(salest) + max(salest)) / 2;
	th2 = mean(salest);
	mu = (1-alpha) / alpha;
	[seeds, label] = seed4rw(salest, th1, th2);
	[~, probabilities] = rrwr(img, superpixels, salest, seeds, label, mu);
	sal = probabilities(:,:,1);
	sal = (sal-min(sal(:)))/(max(sal(:))-min(sal(:)));
    saloutput = zeros(wid(1),wid(2));
	saloutput(wid(3):wid(4),wid(5):wid(6)) = sal;
	saloutput = uint8(saloutput*255);
    
end