You could say that, in terms of implementation, the Integral Channel Features algorithm is divided in three major parts:
- Computation of channels - DONE
- Feature extraction
In the paper, many channels were computed and tested for performance , but once that work is done there is no need to repeat it. Only the ones that achieve the best results were computed. These were the gradient magnitude, gradient histogram channels with 6 bins of orientation and the LUV colour channels.
Now its time to extract features from those channels. Those features constitute of local sums using the integral of the image for fast computation.
I'm not yet sure of how this is going to be done.
PS: I'll be happy to send samples of code to anyone interested