title: How do CNNs process RGB images
body: <p>In a Convolutional Neural Network, the process of convolving is abundant.</p>
  <p>It is known that if you take a 5x5 greyscale image (1 channel) and convolve it with a 3x3 filter (containing certain weights) you get a 3x3 feature map as a result as demonstrated by this picture: Convolutions</p>
  <p>But what happens once you extend this notion of convolving into RGB images whereby now you have 3 channel (R,G,B) to convolve over? Well you simply add a channel to your filter proportional to the # of channel in your original image right? Lets say we did, the process of convolving with an RGB would like the following: a 6x6x3 RGB image convolved with a 3x3x3 filter. This apparently results in a 4x4x1 rather than what one would expect 4x4x3.</p>
  <p>My question is why is this so?</p>
  <p>If you surf the internet for visualizations of feature maps, they return with some form of colorful low &amp; high level features. Are those visualizations of the kernels themselves or the feature maps? Either way, they all have color which means they must have more than 1 channel no?</p>