I am doing a project on vehicle type classification with Neural Networks( classification basis is => sedan,pick up,hatchback,etc type vehicles.)
I am doing image processing for the first time and I have detected about 40 corners using Harris Edge Detection and thus I got a matrix A[40×2].
I am using only this feature as for classification.
Now I want to know how can I use PCA to extract features from it.I know what PCA is and what pca(A) or princomp(A)in matlab will return but I dont get how to use the output of pca function as a feature matrix.
1.Does a feature matrix need to be 1-D array.
2.Should I use principle components array which is the 2nd matrix returned by pca function as a feature matrix (its a 2-D matrix)
3.How can I train Neural Network for 3 classes(hatchback,sedan and pickup).
4.Lastly suppose I have N images for each class to train so do I need to train on each image individually or do I have to create a feature matrix that has a extra dimension = N.
please clear my doubts on these
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