CODEWITHSUNDEEP
matlabimage-processingmatrixcomputer-visionmatlab-cvst
Tariq Khalifa
Tariq Khalifa

Reputation: 152

How to add HOG features into matrix (matlab)

After extracting HOG features of folder of images, I want to add all this results in one matrix. How I can do this? this is my code in matlab:

training_female = 'E:\Training Set\Female Images';

% read all images with specified extention, its jpg in our case
filenames = dir(fullfile(training_female, '*.jpg'));

% count total number of photos present in that folder
total_images = numel(filenames);

for n = 1:total_images

% Specify images names with full path and extension    
full_name= fullfile(training_female, filenames(n).name);

% Read images
training_images = imread(full_name);
[featureVector, hogVisualization] = extractHOGFeatures(training_images);
figure (n)

    % Show all images
    imshow(training_images); hold on;                  
    plot(hogVisualization);
end

Upvotes: 2

Views: 1378

Answers (1)

rayryeng
rayryeng

Reputation: 104525

By looking at the documentation, calling extractHOGFeatures computes a 1 x N vector given an input image. Because it can be a bit cumbersome to calculate what the output size of this may be, which also depends on what parameters you set up for the HOG detector, it's best to first create an empty matrix and dynamically concatenate the features at each iteration. Usually for performance you pre-allocate a matrix if you want to populate the elements on an iterative basis. Not doing it this way gives a slight ding in performance, but it's the most adaptable given your situation. You may want to adjust the HOG parameters and if we do it the dynamic way, that removes the headache of determining what the total size of the matrix should be.

So do something like this. I've placed %//New tags where I've modified your code:

training_female = 'E:\Training Set\Female Images';

% read all images with specified extention, its jpg in our case
filenames = dir(fullfile(training_female, '*.jpg'));

% count total number of photos present in that folder
total_images = numel(filenames);

featureMatrix = []; %// New - Declare feature matrix

for n = 1:total_images

    % Specify images names with full path and extension    
    full_name= fullfile(training_female, filenames(n).name);

    % Read images
    training_images = imread(full_name);
    [featureVector, hogVisualization] = extractHOGFeatures(training_images);

    %// New - Add feature vector to matrix
    featureMatrix = [featureMatrix; featureVector];
    figure(n);

    % Show all images
    imshow(training_images); hold on;                  
    plot(hogVisualization);
end

featureMatrix will contain your HOG features where each row is for each image. Therefore, for a particular image i, you can determine the HOG features by:

feature = featureMatrix(i,:);

Caveat

I need to mention that the above code assumes that all images in your directory are the same size. If they're not, then the output vector size for each HOG call is going to be different. If that's the case, you'll want to have a cell array to adapt for the different sizes.

Therefore, do something like this:

training_female = 'E:\Training Set\Female Images';

% read all images with specified extention, its jpg in our case
filenames = dir(fullfile(training_female, '*.jpg'));

% count total number of photos present in that folder
total_images = numel(filenames);

featureMatrix = cell(1,total_images); %// New - Declare feature matrix

for n = 1:total_images

    % Specify images names with full path and extension    
    full_name= fullfile(training_female, filenames(n).name);

    % Read images
    training_images = imread(full_name);
    [featureVector, hogVisualization] = extractHOGFeatures(training_images);

    %// New - Add feature vector to matrix
    featureMatrix{n} = featureVector;
    figure(n);

    % Show all images
    imshow(training_images); hold on;                  
    plot(hogVisualization);
end

To access a particular image's features, or image i, do:

feature = featureMatrix{i};

Upvotes: 2

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