Ideal number of HoG features

Question

So there many options of how one can extract HoG features. Using different orientations, different numbers of pixels per cell and different block sizes.

But is there a standard or optimal configuration? I have training images of size 50x100, and I'm opting for 8 directions of orientation. I'm extracting the features from training data in order to do vehicle classification. But I really don't know what's "optimal".

For example, I have 2 configurations here, is there any reason to choose one over the other? Personally I feel like the second one is a better choice, but why?

Answer 1

I used HOG for product recognition. From what I understood at the time, you are pointing to a real problem of the standard HOG. There is simply no optimal configuration, it depends on the dataset. If you have the optimal values for your dataset, and then resize all the pictures of your dataset, you should resize your values too. Thus, there is no optimal "one size fits all" values for HOG.

But all is not lost. What you should do instead is a method that works "all the time". The idea is to do Spatial Pyramid Matching. This is just doing HOG at various scales and combining them together. A picture being worth a thousand words :

You can see that here, level 2 is just the standard HOG with fine cells. But perhaps it is not the best scale (because the cells are too small and you just observe noise) (On the other hand, too large cells, like level 0, may be too large, and you will have uniform histograms everywhere). You can compute the best weights for each level when you do the training on your dataset, and you will know what are the optimal values, i.e : what is the most relevant cell size