Convolutional Neural Networks (CNNs) has been designed specifically for image recognition. Each image used in learning is divided into compact topological portions, each of which will be processed by filters to search for particular patterns. Formally, each image is represented as a three-dimensional matrix of pixels (width, height, and color), and every sub-portion is put on convolution with the filter set. In other words, scrolling each filter along the image computes the inner product of the same filter and input. This procedure produces a set of feature maps (activation maps) for the various filters. By superimposing the various feature maps of the same portion of the image, we get an output volume. This type of layer is called a convolutional layer.

The following figure shows a typical CNN architecture: