In a previous post I explained how to produce color or intensities histogram of different regions of an image. For example Fig 1, Fig 2 and Fig 3 presents the regions for three different type of region divisions: 1, 3, 8 respectively.
The basic goal was to produce small subimages of aproximately the same size and then calculate the histogram over the subimage. In this post I will use the existent function regionImHistogram to produce features for a set of images that are in the same directory. The images are numerated in the directory to simplify the process and to track it back to the data I have in other tables
For each image I am going to produce a lot of features. The basic idea is to produce histograms of many regions. Concerned of the size of the descriptor I am going to stop using 256 bins for the histograms. Instead of that I am going to use different quantities of bins depending on the regions I am dividing the image. If I have more regions, I will use less bins. More regions also means less pixels, so maybe this will give a little bit more of generalization or statistical power to the feature. Here is the code for 6 different region divisions.
% Parameters: % - directory with the images % - number of images on the directory % - name of the file with the features function extract_cohs(dir, samples, filename) % The different amount of regions the image % is going to be divided fibs = [1,2,3,5,8,13]; % The bins per region bins = [128, 64, 32, 16, 8, 4]; % A counter of the number of features added total = 0; % The ranges that indicate were the set of features % per region division are going to be saved ranges = [6, 2]; % This cycle calculates the ranges in the vector for fib = 1:size(fibs)(2) ranges(fib,1) = total + 1; total += 3*fibs(fib)*fibs(fib)*bins(fib); ranges(fib,2) = total; endfor % create the vector that is going to keep all the samples histo = zeros(samples, total); % open each image and process it for ind = 1:samples im = imread(strcat(dir, int2str(ind))); for fib = 1:size(fibs)(2) histo(ind,ranges(fib,1):ranges(fib,2)) = regionImHistogram(im, fibs(fib), bins(fib)); endfor endfor % save the features save("-text", filename, "histo"); % save the values of the ranges save("-text", "ranges.dat", "ranges");
The previous code will generate 6780 features per image and depending on the quantity of images it could take a while. It’s quite straight forward to calculate intensity histograms from this code. Two changes are necessary:
1. Instead of
total += 3*fibs(fib)*fibs(fib)*bins(fib);
You have to take out the 3*
total += fibs(fib)*fibs(fib)*bins(fib);
im = imread(strcat(dir, int2str(ind)));
You have to transform the image to grayscale
im = imread(strcat(dir, int2str(ind))); if isrgb(im) im = rgb2gray(im) else
I ll be posting some code to produce edge orientation histogram very soon.