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More questions on feeding HoG features to CvSVM

I've managed to extract HoG features from positive and negative images (from INRIA's person dataset ) using OpenCV's HOGDescriptor::compute function.

I've also managed to pack the data correctly and feed it into CvSVM for training purposes.

I have several questions:

  • While extracting features, I used positive images with dimension of 96 x 128, while the negative images are on average 320 x 240. I have been using window size of 64 x 128 for HoG extraction, should I use other window size ?

  • The size of extracted features for positive images are around 28800 features, while the negative ones are around 500000+. I have been truncating the features from negative ones to 28800, I think this is wrong, since I believe I'm losing too much information when feeding these features to SVM. How should I go and tackle this ? (It seems like I can only feed the same sample size for negative and positive features)

  • While doing prediction on images bigger than 64 x 128 (or 96 x 160), should I use a sliding window to do prediction ? Since large negative images still gives me more than 500000 features, but I can't feed it into SVM due to sample size.

Thanks a lot in advance !