How to process extracted SURF features for SVM classifier

2019 12 17
5 个回答
回答已采纳
更新时间：2020 4 11
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Bilal Razi
Bilal Razi 2020-4-11

0 个投票

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.

更多回答（4 个）

Divya Gaddipati
Divya Gaddipati 2020-1-14

0 个投票

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!
Amit DOegar
Amit DOegar 2020-1-16

0 个投票

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
Amit DOegar
Amit DOegar 2020-2-3

0 个投票

Features set is in matrix form
using rehape function we can convert into vector
but dimensions are tool large, still looking for solution

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