Convolutional Neural Network

What is Convolutional Neural Network?

A convolutional neural network (CNN) is a type of artificial neural network used in image recognition and processing that is specifically designed to process pixel data.

CNNs are powerful image processing, artificial intelligence (AI) that use deep learning to perform both generative and descriptive tasks, often using machine vison that includes image and video recognition, along with recommender systems and natural language processing (NLP).

A neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Traditional neural networks are not ideal for image processing and must be fed images in reduced-resolution pieces. CNN have their “neurons” arranged more like those of the frontal lobe, the area responsible for processing visual stimuli in humans and other animals. The layers of neurons are arranged in such a way as to cover the entire visual field avoiding the piecemeal image processing problem of traditional neural networks.

A CNN uses a system much like a multilayer perceptron that has been designed for reduced processing requirements. The layers of a CNN consist of an input layer, an output layer and a hidden layer that includes multiple convolutional layers, pooling layers, fully connected layers and normalization layers. The removal of limitations and increase in efficiency for image processing results in a system that is far more effective, simpler to trains limited for image processing and natural language processing. 

Example for the CNN would be Video surveillance

Components of CNN

The CNN model works in two steps: feature extraction and Classification

Feature Extraction is a phase where various filters and layers are applied to the images to extract the information and features out of it and once it’s done it is passed on to the next phase i.e Classification where they are classified based on the target variable of the problem.

A typical CNN model looks like this:

• Input layer
• Convolution layer + Activation function
• Pooling layer
• Fully Connected Layer

    Source: https://learnopencv.com/image-classification-using-convolutional-neural-networks-in-keras/

Let’s learn about each layer in detail.

Input layer

As the name says, it’s our input image and can be Grayscale or RGB. Every image is made up of pixels that range from 0 to 255. We need to normalize them i.e convert the range between 0 to 1  before passing it to the model.

Below is the example of an input image of size 4*4 and has 3 channels i.e RGB and pixel values.

Convolution Layer

The convolution layer is the layer where the filter is applied to our input image to extract or detect its features. A filter is applied to the image multiple times and creates a feature map which helps in classifying the input image. Let’s understand this with the help of an example. For simplicity, we will take a 2D input image with normalized pixels.

In the above figure, we have an input image of size 6*6 and applied a filter of 3*3 on it to detect some features. In this example, we have applied only one filter but in practice, many such filters are applied to extract information from the image.

The result of applying the filter to the image is that we get a Feature Map of 4*4 which has some information about the input image. Many such feature maps are generated in practical applications.

Let’s get into some maths behind getting the feature map in the above image.

As presented in the above figure, in the first step the filter is applied to the green highlighted part of the image, and the pixel values of the image are multiplied with the values of the filter (as shown in the figure using lines) and then summed up to get the final value.

In the next step, the filter is shifted by one column as shown in the below figure. This jump to the next column or row is known as stride and in this example, we are taking a stride of 1 which means we are shifting by one column.

Similarly, the filter passes over the entire image and we get our final Feature Map. Once we get the feature map, an activation function is applied to it for introducing nonlinearity.

A point to note here is that the Feature map we get is smaller than the size of our image. As we increase the value of stride the size of the feature map decreases.

Pooling Layer

The pooling layer is applied after the Convolutional layer and is used to reduce the dimensions of the feature map which helps in preserving the important information or features of the input image and reduces the computation time.

Using pooling, a lower resolution version of input is created that still contains the large or important elements of the input image.

The most common types of Pooling are Max Pooling and Average Pooling. The below figure shows how Max Pooling works. Using the Feature map which we got from the above example to apply Pooling. Here we are using a Pooling layer of size 2*2 with a stride of 2.

The maximum value from each highlighted area is taken and a new version of the input image is obtained which is of size 2*2 so after applying Pooling the dimension of the feature map has reduced. 

Fully Connected Layer

Till now we have performed the Feature Extraction steps, now comes the Classification part. The Fully connected layer (as we have in ANN) is used for classifying the input image into a label. This layer connects the information extracted from the previous steps (i.e Convolution layer and Pooling layers) to the output layer and eventually classifies the input into the desired label.

The complete process of a CNN model can be seen in the below image.

*References: analyticsvidhya.com