Image Classification and Recognition Using Self-Organizing Feature Maps Neural Network

In this article, we will demonstrate the application of Self-Organizing Feature Maps (SOM) neural networks for image classification and recognition. SOM is an unsupervised neural network algorithm that automatically learns and organizes the features of input data to classify and identify images. This tutorial will provide a detailed explanation of SOM's working principles, including its competitive learning mechanism and neighborhood function that enables topological preservation of high-dimensional data. We will implement key algorithmic steps such as weight initialization, similarity calculation using Euclidean distance, and iterative weight updates through vector quantization. The practical section includes code examples demonstrating how to preprocess image data, train the SOM model with adjustable learning rates and neighborhood radii, and visualize the resulting feature maps using U-matrix techniques. By studying and implementing this tutorial, readers will gain deeper insights into image classification methodologies and be able to apply SOM algorithms to real-world problems. This guide aims to support researchers and practitioners in advancing their work in the field of image recognition and pattern analysis.