Feature extraction and suitable machine learning model

When dealing with large datasets with many columns and variables, feature extracting is used to divide and reduce existing data into a manageable group.

But for image processing, machines can't extract features such as edges, shapes, or even size in this way. Instead, machines see and store an image in the form of a matrix of numbers. It gets more complicated when dealing with large images and datasets and eventually leads to the Curse of Dimensionality. Instead of processing each matrix, the machine will discard some features to more accurately map in a lower-dimensional space.