Classification of Images Using Support Vector Machine (SVM) Approach

Abstract

The article explains how to classify images using machine learning techniques. The Support Vector Machine with strong flexibility and the capacity to operate with a vast collection of input data was employed to complete this challenge. A software created in the MATLAB simulation environment was used to explain the model. The main difficulty that image classification is gathering a large enough training set of photos to obtain a high probability of successful recognition. The photographs in the CIFAR 100 database, which have a tiny size of 32x32 pixels and are publicly available. It has 60 000 photos organised into ten primary categories. The author's database was then utilised, which included 1000 pedestrians, autos, and road signs.