Hello. 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(:)));Hello. 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(:))); Hello. 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(:))); computer vision, computer science MATLAB Answers — New Questions

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