The classic Neural Network of Machine Learning usually use fully-connection, which will cost too much computing resource to get final result if the inputs are high-resolution images. So comes the Convolutional Neural Network. CNN (Convolutional Neural Network) splits the whole big image into small pieces (called Receptive Fields), and do some “Convolutional Operations” (actually are some image transformations, also called Kernels) on each Receptive Field, then the pooling operation (usually max-polling, which is simply collect a biggest feature weight in a 2X2 matrix).
Receptive Fields is easy to understand, but why do it use different kind of “Convolutional Operations” on them? In my opinion, “Convolutional Operations” means using different kind of Kernel Functions to transfer the same image (for example: sharpen the image, or detect the edge of object in image), so they could reveal different views of the same image.
These different Kernel Functions review different “Features” of a image, thus we call them “Feature Maps”:
(The matrix of light-yellow is just transferred from light-gray matrix on its left)
By using Receptive Fields and max-pooling, the number of neurons will become very small gradually, which will make computing (or regression) much more easy and fast:
Therefore, I reckon the main purpose of using CNN is to reduce the difficulty of computing result of a fully-connected Neural Network.