I have a basic question regarding convolutional neural network. Assume I have a set of 1000 RGB images and I train a CNN from this set. I can obviously split each of my RGB images into 3 different greyscale images, each representing red, green, and blue array - thus creating 3000 greyscale images.
My question is: if I train a CNN from those 3000 greyscale images, would I get the same parameters from the first CNN? More precisely, how does CNN behave when we feed it with RGB images? Does it extract the combined features from the RGB images or does it merely learn the features in each R,G,B channel separately?