A Comparative Analysis of Image Classification Classifiers

The most significant and difficult issue in computer vision is classification. The characterization, structure, or likeness of objects is used to classify them. The categorization of images into one of several designated groups is known as image classification. An image is expressed by units called pixels. A method called image classification interprets an image and derives data from it that could be applied to other tasks. Using Machine Learning (ML) techniques to address the complicated problem of image classification presents a challenge in today’s environment. The K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), Convolutional Neural Network (CNN), Support Vector Machine (SVM), ISODATA and Random Forest classifier methods are used to tackle these objectives in the study. In addition to outlining each technique’s benefits and drawbacks, this overview offers theoretical background on a range of classification methodologies. This study can aid in leveraging the efficiency of neural networks for classifying tasks that call for non-binary classifications, which is a common requirement when actual statistics is taken into account.