What is Convolutional Neural Network? | What is a Convolutional Filter?

It is one of the classes of deep neural network which is inspired by the visual system in mammal’s brain. It feeds forward multi-layered neuronal network and fully connected, although CNN is a very simple network connection, nevertheless, it is structured in a very special way that used entanglement/complexity/convolution in place of general matrix multiplication in at least one of the layers. Convolutional Neural Network is designed to recognize visual patterns from image pixels with minimum pre-processing; hence it is best for image processing techniques.

In the convolutional neural network, we extract useful features from the input data set.

Ex: In image processing, there is a huge range of filters, each type of filters helps to extract different features from the input image

Convolutional or Early Layer: gives only a little information about the image like orientation - vertical, horizontal or diagonal edges, etc.

Intermediate Layer: is the second layer, which is more specific and gives information about the smallest feature.

Deep Layer: is the last layer and it is extremely specific by combining the entire feature extracted by previous layers.

What is a Convolutional Filter?

A Convolutional filter detects the template or pattern in the input images. It is a 2-D matrix of learnable weights, so the value of weight keeps changing throughout the training.  The backpropagation algorithm is used in learning the weight. The number of filters depends on the number of neurons, and each filter detects only one type of patterns for example - Edge, circle, slope, or specific object like eyes, ear, nose and in the deeper layer filters are able to detect complete image like dog, cat, etc.

Convolution matrix is calculated by the sum of the product of the corresponding elements of the two matrixes.