How to find HIT RATE(HR) and FALSE ALARM RATE(FAR) of an image from its saliency map?

The codings that i used to find the saliency map and object map of a given input image is as follows:

%% Read image from file inImg = imread('60.jpg'); inImg = im2double(rgb2gray(inImg)); inImg = imresize(inImg, [64, 64], 'bilinear'); imshow(inImg);figure; %% Spectral Residual myFFT = fft2(inImg); myLogAmplitude = log(abs(myFFT)); myPhase = angle(myFFT); mySmooth = imfilter(myLogAmplitude, fspecial('average', 3), 'replicate'); mySpectralResidual = myLogAmplitude - mySmooth; saliencyMap = abs(ifft2(exp(mySpectralResidual + i*myPhase))).^2; %% After Effect saliencyMap = imfilter(saliencyMap, fspecial('disk', 3)); saliencyMap = mat2gray(saliencyMap); imshow(saliencyMap, []);figure; %% Object Map t = mean(mean(saliencyMap))*3; objectMap=saliencyMap>t; imshow(objectMap, []);

After this, inorder to find HR and FAR, the following theory is given:

For each input I(x), the binary image obtained from kth hand-labeler is denoted as Ok(x), in which 1 denotes for target objects, 0 for background. Given the generated saliency map S(x), the Hit Rate (HR) and the False Alarm Rate (FAR) can be obtained as shown in the attached image:

This criterion states that an optimal saliency detection system should response low in regions where no handlabeler suggests proto-object, and response high in region where most labelers meet at an consensus of proto-objects.