I have some problems on understanding filter multiplication in CNN.
Let's assume we have an 32x32x3 image which is in RGB Colour system. We want to create 96 feature map. Which dimension is used while this filter multiplication occurs? I mean, image has three layers, but after one time filter multiplication, we have WxHx1 (Width,Height -based on CNN parameters). Why and how output has only one dimension? Which dimension(R or G or B) is chosen when filter is multiplied by image? It should be random, not much information on the web about it.
Also, a little question, we created 96 feature map, and then we apply another convolution layer which also says 96 feature map. I think that for one image created by previous convolutional layer, 96 feature map is created for one of the 96. In a nutshell, there are 96*96=9216 image after second convolution layer, right?