APPROACHES TO IMAGE CLASSIFICATION USING MACHINE LEARNING

This article examines the subject of image classification, which is one of the fundamental and rapidly developing areas in computer vision and machine learning. The present paper compiles and analyzes various classification methods, ranging from simple supervised learning techniques to efficient convolutional neural networks (CNNs) in deep learning. The study investigates the performance of different algorithms, such as K-NN, SVM, Random Forest, and CNN, on specific classification tasks, including fruit classification, medical diagnosis, animal recognition, and image scene classification (landscape, city). The research results show that modern methods, especially deep learning, provide the highest accuracy in object recognition, which includes, for example, high accuracy in automated process diagnostics. The article also discusses practical applications of these technologies in various fields, ranging from medicine and agriculture to object detection in satellite images and automatic classification of visual databases. This article is a valuable resource for researchers, engineers, software developers, and practitioners working in the field of computer vision and image analysis.

machine learning, classification, supervised learning, image recognition, multi-purpose classification, content-based image retrieval, face recognition, handwritten text recognition.

2026-03-30