Htykuut

Thanks for reading my question. Sorry if it's an easy question but I'm new in Matlab and Computer Vision. I'm trying to extract features from training images. I need to use histogram of color, histogram of gradient, SIFT-like features and compare their performance. I'm lost and have spent days to find a good way to use. How would you extract features in this example? I have put %Start and %End where I need to do that.

A written code would be greatly appreciated.

% Step-1: Load training and test data using imageSet.

imageDir   = fullfile('./data');



% imageSet recursively scans the directory tree containing the images.

flowerImageSet = imageSet(imageDir,   'recursive');





%% step-2: Partition the data set into a training set and a test set. 



tmparr=randperm(80);

arrTrainID=tmparr(1:50); %training ID; 

arrTestID=tmparr(51:end);% testing ID;  





%% Step-3: Train the classifier using features extracted from the training set.



% Step 3.1 Extract features from training images



trainingFeatures = ;

trainingLabels   = ;

featureSize =128; 

for flower = 1:numel(flowerImageSet)



    numImages = numel(arrTrainID);           

    features  = zeros(numImages, featureSize);



    for i = 1:numImages



        img = rgb2gray(read(flowerImageSet(flower), arrTrainID(i)));



        %START      

        features(i, :) = rand(1,featureSize);

        %END

    end



    % Use the imageSet Description as the training labels.  

    labels = repmat(flowerImageSet(flower).Description, numImages, 1);



    trainingFeatures = [trainingFeatures; features];  

    trainingLabels   = [trainingLabels;   labels  ];  



end



%%

% Step 3.2, train a classifier using the extracted features. 



classifier = fitcecoc(trainingFeatures, trainingLabels);



%% Step 4: Evaluate the Flower Classifier



testFeatures=;

testLabels=;

% Step 4.1: Loop over the testing images and extract features from each image. 

for flower = 1:numel(flowerImageSet)



    numImages = numel(arrTestID); %     

    features  = zeros(numImages, featureSize);    

    for i = 1:numImages        

        img = rgb2gray(read(flowerImageSet(flower), arrTestID(i)));  

        %Start    

        features(i, :) = rand(1,featureSize);

        %End      

    end



    % Use the imageSet Description as the training labels.  

    labels = repmat(flowerImageSet(flower).Description, numImages, 1);        

    testFeatures = [testFeatures; features];  

    testLabels   = [testLabels;   labels  ];  

end



% Step 4.2: Make class predictions using the test features.

predictedLabels = predict(classifier, testFeatures);



% Step 4.3: Tabulate the results using a confusion matrix.

confMat = confusionmat(testLabels, predictedLabels);



% Step 4.4: calculate accuracy

fprintf('accuracy=%f',sum(diag(confMat))/sum(confMat(:)));