Preprocess Image Data For Machine Learning

In recent years, there have been huge advances in the field of neural networks specifically to train models for image recognition and classification. To design and implement an efficient and powerful image classification solution. You need to know how such image data can be read into your program, and how you can represent images as tensors to feed into your model to get the best out of ML algorithms.

Image representation:

All images can be represented using pixels. images are essentially just matrices where the individual cells of the matrix contain pixel data. An image can be divided into a grid of pixels. Each element of this grid is used to hold pixel information, and the value of a pixel depends on the type of image that you’re working with,This pixel representation of image matrices can be fed into a machine learning model such as those built using neural networks. These neural networks will identify edges, colors, and shapes within the images, and then perform image recognition. It can perform classification or identify and detect objects within an image.

Color image:

Color or RGB image, every pixel is represented using R, G, and B values. This means that you need three numbers to represent an image, a pixel that is red in color will be represented as 255, 0, 0. The…