feature extraction using PCA
My job is to perform gesture recognition. I want to do that by training a support vector machine using the features extracted by performing PCA(Principal component Analysis). But I'm getting a little confused about the procedure.
After going through various articles, I've figured out these steps.
- Take 'd' number of images(n*n) of the same gesture.
- Convert each n*n image into a sigle row.
- Form a matrix of order d*(n*n).
- Compute the eigen values & eigen vectors.
- Use top 'k' e开发者_如何学编程igen vectors to form a subspace.
- Project the image from original n*n dimension to 'k' dimension.
Question:
1) I have a set of 100 gestures and performing above 6 steps will give me 100 subspaces.My testing should be done on a realtime video to find which class a gesture falls in. Onto which supspace do I project each video frame to reduce the dimension for feeding it to the classifier?
Thank you in advance.
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