What is Convolution in Computer Vision

Introduction

In machine learning, computer vision is a field where image datasets are used and analyzed to perform several complex tasks related to the same. Here different algorithms and techniques are used related to handling and analyzing the images in order to use the data and train high-performing models.

Convolution is a very important term or a phenomenon that occurs in the name of Convolutional neural networks, which is the most famous technique used for handling and dealing with image datasets in machine learning. In this article, we will discuss convolution, what are convolutional operations, and other important things related to the same.

So before directly jumping into the convolutional, let us discuss a bit about computer vision first.

What is Computer Vision?

In deep learning, computer vision is a branch that involves various complex algorithms and techniques which are used to load, handle, preprocess, and analyze the image dataset, which is to be used for training the final model. Computer vision involves multiple famous tasks such as object detection, image segmentation, face recognition, etc.

For computer vision, convolutional neural networks are used, which are the type of neural networks which deal with image datasets. It is capable of accepting the images as input, loading them, preprocessing them, and applying different techniques to extract the information from the same.

The convolutional neural networks are fundamentally the same as the artificial neural networks; just here, the term artificial is replaced by the4 convolution, which directly means that the convolutions or convolutional operations are involved in these techniques.

Now let us discuss the convolutional operations in computer vision.

Convolutional Operations

As we know that in machine learning and deep learning, the quality and the quantity of the data are one of the most important and influencing parameters to the performance of the model. Hence the quality and quantity of the data should remain well in order to achieve the high performing and reliable model.

Although, once we have a good amount of quality data, it is not over; the main thing is to get useful information from the data in order to make the model aware of the things. To do this, various data cleaning and preprocessing techniques are applied, which cleans and preprocess the data and extract the various information and features from the data.

Extracting features or information from normal text or numerical data is very easy compared to image datasets. In the case of image datasets, different filters are applied with several other parameters in order to preprocess and analyze the image. Let us discuss how the convolution operations take place in neural networks.

How Are Convolutional Operations Performed?

In computer vision, we know that convolutional operations are performed mainly for feature extraction, which helps in getting useful information from the image datasets. The main parameter while performing the convolutional operation is the kernel or a filter that we are using to create the feature map of the image.

Let us suppose we have an image as an input, and we want to train a model on the same. Now this image will be first passed to the input layer. After going through the input layer, the image will go to the first convolutional layer or a first hidden layer. Here the convolutional layer will have its own different parameters like a filter, kernel size, paddings, strides, activation functions, etc.

So when the first layer of the convolution network receives the input image, it will take the image and will apply the kernel or a filter to the same. Here the filter may be of any size, which will be basically put on the original image, and according to the operations we want to perform, either sum, average, min, or max, we will convert those pixel readings of the original image to less dimensional pixel readings.

So suppose we had an image of 64*64 size and we applied a filter of 3*3, then the final size of the image after going their high the first layer will be 62*62.

The below formula can be used to get the output image size after going through or preprocessing from the convolutional layers.

Image Size = n - f + 2p/s + 1

Where n is the original size of the image, f is the filter size, p is the padding, and s is the strides that we are using in a particular convolutional layer.

Here note that a convolution layer refers to the single layer of convolution operations, and a complete can have multiple convolution layers having their own set patterns. These parameters can be tuned according to the performance of the model, and the number of layers we are using can also be tuned considering the performance and complexity of the model.

The deeper we go into the convolutional networks, the model detects very complex and smaller things from the image while the initial layers or kernels of the convolutional networks perform easier tasks such as edge detection, larger object detections, etc.

Convolutional Layer Parameters

Number of Filters: This represents the number of filters we want to apply to the image to preprocess and extract the features from the image.

Kernel Size: This parameter represents the size of the shape of the filter that we want to apply to the image in order to extract the features from the image.

Activation Function: This parameter represents the activation function we want to use in a particular convolutional layer. Here we can use any activation function that fits the model, like relu, softmax, sigmoid, tanh, etc.

Padding: The padding refers to the number of extra pixel layers we want to add to the image in order not to lose any information or the size of the image.

Striders: The strides refer to the convolutional steps tanh is being taken by the filter while performing the convolutional operation on the original image.

Conclusion

In this article, we discussed the convolutions, what are the convolutional operations, how they are performed, and some parameters related to the same. This article will help one to understand convolutional operations better and will help one to apply the understanding while performing the same.