Reflective Vision: Detecting Visual Similarities And Changes Using Siamese Neural Architectures

Abstract

his paper explores the application of deep learning techniques, specifically Siamese Neural Networks (SNN), for image processing tasks aimed at detecting similarity and changes between images. The SNN architecture, designed to process pairs of images, learns a shared embedding space where similar images are closer and dissimilar ones are further apart. This unique capability makes SNNs particularly effective for tasks such as facial recognition, object tracking, and change detection in various domains, including medical imaging and surveillance. Our approach involves training the network on labeled image pairs, enabling it to discern subtle differences and similarities with high accuracy. Experimental results demonstrate the robustness of SNNs in identifying both minor and major changes across diverse datasets. The findings suggest significant potential for SNNs to advance the state-of-the-art in image processing applications, providing a reliable tool for automated visual analysis. Deep learning has revolutionized image processing, offering powerful tools for analyzing and interpreting visual data. Among these tools, Siamese Neural Networks (SNNs) have emerged as a robust architecture for tasks that require detecting similarity and changes between images. Unlike traditional convolutional neural networks (CNNs) that operate on single images, SNNs are designed to process pairs of images, learning a shared embedding space where the distance between embeddings reflects the similarity of the input images. The Siamese architecture consists of two identical subnetworks that share weights and parameters, ensuring consistent feature extraction from both images in a pair. During training, the network learns to produce similar embeddings for similar images and distinct embeddings for dissimilar ones. This capability makes SNNs particularly suitable for applications such as facial recognition, where the goal is to determine whether two images represent the same person, or for change detection, where the objective is to identify differences between images taken at different times or under varying conditions.

In this context, our research focuses on leveraging SNNs for various image processing tasks, demonstrating their effectiveness in recognizing subtle differences and similarities across a range of applications. Through rigorous experimentation and analysis, we highlight the advantages of using SNNs over conventional methods, showcasing their potential to significantly enhance automated visual analysis in fields such as medical imaging, surveillance, and beyond.