How to process extracted SURF features for SVM classifier

How to process stored surf features of multiple files for svm classifier

Answer 1

[Bilal Razi]

You can use a combination of functions.

bagOfFeatures and trainImageCategoryClassifier

Use bagOfFeatures to extract your SURF features e.g.

bag = bagOfFeatures(imds, "CustomExtractor", extractorFcn);

where extractorFcn is is a function which extracts your SURF features

then train your model using SVM

classifier = trainImageCategoryClassifier(imds, bag, "LearnerOptions", opts);

where opts = templateSVM()

Hope that helps.

Answer 2

[Divya Gaddipati]

You can use fitcsvm to train SVM classifier.

You can load the files into the workspace in a loop.

for i = 1 : total_files
    x = load(filename(i).name);
    XTrain(i,:) = x;
    clear x;
end

Assuming your labels are in a variable YTrain, you can use the fitcsvm as follows:

Mdl = fitcsvm(XTrain, YTrain)

For more information on fitcsvm, you can refer to the following link:

https://www.mathworks.com/help/stats/fitcsvm.html

Alternatively, you can also use classificationLearner

https://www.mathworks.com/help/stats/classificationlearner-app.html

Hope this helps!

Answer 3

[Amit DOegar]

I mean to ask for classfier needs input as vector of features and label

as input for classifier,

but when we extract surf features it gets into array for example

extracted features of aray size 100*100 then through reshape if we convert into vector

size will be of 100* 100 i.e 10000 for a vector . so how to use any classfier for the features like surf, mser or sift, brisk and give it to classifiier

Answer 4

[fadi ssuhimat]

Do you find soluation for this case, I have same issue???

Answer 5

[Amit DOegar]

Features set is in matrix form

using rehape function we can convert into vector

but dimensions are tool large, still looking for solution