Source Code for Image Clustering and Fusion Based on Self-Organizing Neural Networks

This project provides a complete source code implementation for image clustering and fusion based on self-organizing neural networks. Self-organizing neural networks are computational models that simulate the interactive behavior of neurons in the human brain, employing learning and adaptive mechanisms to perform image clustering and fusion tasks. The source code includes implementations of key algorithms such as competitive learning, neighborhood functions, and weight adaptation mechanisms that enable unsupervised pattern recognition. The program offers researchers and developers a robust foundation for conducting research and developing applications in image processing and computer vision domains. It features high flexibility and scalability, allowing customization and enhancement according to diverse application requirements. The implementation provides multiple algorithmic options including different distance metrics (Euclidean, Manhattan), learning rate schedules, and neighborhood size adaptation strategies. Users can configure parameters through configuration files or API interfaces, and optimize performance using built-in validation metrics. The code structure follows modular design principles, with separate modules for network initialization, training cycles, cluster visualization, and fusion operations, facilitating easy integration and extension.