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.

Fig 1. 1x1 region divisions
Fig 2. 3x3 region divisions
Fig 3. 8x8 region divisions

 

 

 

 

 

 

 

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);

2. After

    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.

One thought on “Lots of features from color histograms on a directory of images (0ctave/Matlab)

  1. Pingback: Lots of features from edge orientation histogram on a directory of images | The Digital Fingerprint of the Brush

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