An Overview of Computer Vision Techniques for Image Retrieval

Finding comparable photos using a query is known as image retrieval, and it has proven essential to many applications. This review explores the development of computer vision methods that allow for the effective retrieval of visual content. We examine the groundwork established by feature-based methods such as SIFT, which completely changed the field of image retrieval. We explore methods such as hierarchical deep metric learning and contextual pooling, demonstrating how they might take use of spatial linkages to increase retrieval accuracy. The research also investigates the potential of Generative Adversarial Networks (GANs) for image retrieval tasks by allowing picture alteration for more sophisticated searches. We may learn a great deal about the state-of-the-art and set the path for future directions in computer vision and image retrieval research by looking at these developments.