有关OpenCV的HoG和CvSVM的更多问题

Question

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.

Answer 1

Why you can't just resize all your patches to the same size? Hog descriptor depends on windows size, blocks and cells sizes. You should try different combinations. With small cells you can capture small details, but you will lose in generality and vice versa. 1.) Don't understand the question 2.) Make all descriptors the same size, extracting hog from resized images. 3.) Don't understand the question