MATLAB: Error :subscripted assignment dimension mismatch when use extractSURFFeature

detectsurffeatures detectbriskfeatures extracthogfeatures

when i extract surf or fast feature , i have error but when i used extractHOGFeatures i do not have error

for i = 1:numel(trainingSet.Files)
I = readimage(trainingSet, i);
I=imresize(I,[1024 1024]);
I = rgb2gray(I);
train=detectSURFFeatures(I);
[trainfeatures(i,:), trainpoints(i,:)]= extractFeatures(I,train.selectStrongest(10));
end
Error: subscripted assignment dimension mismatch

how can i use extractSURFFeatures and detectBRISKFeatures instead of extractHOGFeatures

Best Answer

For SURF:

https://www.mathworks.com/help/vision/ref/extractfeatures.html#output_argument_features

"M-by-N matrix | binaryFeatures object

Feature vectors, returned as a binaryFeatures object or an M-by-N matrix of M feature vectors, also known as descriptors. Each descriptor is of length N."

For HOG:

https://www.mathworks.com/help/vision/ref/extracthogfeatures.html

"features = extractHOGFeatures(I) returns extracted HOG features from a truecolor or grayscale input image, I. The features are returned in a 1-by-N vector, where N is the HOG feature length"

Notice that HOG returns a vector, but SURF returns a 2D array.

You could assign the SURF features to a variable and reshape that to a vector for storing.

Related Solutions

MATLAB: How to use Extract interest point descriptors in videos of folders

Hello,

I am assuming that you are making use of a Neural Network for classification purpose.

In order to train Network using video, extract all the frames from each video and create an imageset for all frames. You may make use of HOG or SURF features from the frames and fitcecoc can be used to classify the frames using the description of each imageset as a label.

Following code may be implemented to make a row-wise matrix for frames using SURF features as descriptors.

for i=1:length(frames) 
 st=strcat('D:\test\chair\000',num2str(i,'%02d'),'.png'); %%Directory to image set
    I = imread(st); 
    I = rgb2gray(I); 
    points = detectSURFFeatures(I); 
    [features, valid_points] = extractFeatures(I, points); 
    [s1,s2]=size(features); 
    S=s1*s2; 
    ftr=reshape(features,1,S); 
    F(i,:)=ftr; 
end

Please refer to the following links:

https://www.mathworks.com/help/deeplearning/gs/create-simple-deep-learning-classification-network.html (Basic image classification using a simple convolution Neural Network)
https://www.mathworks.com/videos/what-is-deep-learning-toolbox--1535667599631.html (Video)
https://in.mathworks.com/help/deeplearning/ug/classify-videos-using-deep-learning.html (Classify Videos using Deep Learning)
https://in.mathworks.com/matlabcentral/fileexchange/74402-video-classification-using-lstm-lstm (Video Classification using LSTM)
https://www.mathworks.com/help/vision/examples/image-category-classification-using-bag-of-features.html (Image category classification using a "bag of features" approach which uses SURF features and SVM classifier)
https://in.mathworks.com/help/vision/ref/extracthogfeatures.html (HOG Feature)
https://in.mathworks.com/help/stats/fitcecoc.html (fitcecoc)

Hope it helps!!

MATLAB: Error in storing HOG feature

extractHOGFeatures returns different sized features based on the image size. So you will need to resize images to the same image size if you expect the same feature length as follows:

I = imread(filename);
I = imresize(I,[256 256]); % align image size
featureVector = extractHOGFeatures(I);

Best Answer

Related Solutions

MATLAB: How to use Extract interest point descriptors in videos of folders

MATLAB: Error in storing HOG feature

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