An Insight into Content-Based Image Retrieval Techniques, Datasets, and Evaluation Metrics

The goal of a content-based image retrieval (CBIR) framework is to enable users to efficiently retrieve images from a large database based on the visual content of the images, rather than relying on metadata or annotations. CBIR systems are becoming more and more popular, finding their applications in a wide variety of fields like heath care, e-commerce, law enforcement, and searching digital libraries. Computing machines with CUDA architecture have powered deep learning-based techniques for efficient CBIR and, as such, CBIR systems have become fast with more accurate query results. This work is intended to provide an introduction to CBIR systems and different feature and learning-based techniques to perform CBIR. An overview of different datasets, evaluation metrics, and pros and cons of different CBIR techniques is presented. The paper concludes by discussing current research challenges and future opportunities to improve and apply CBIR to various fields.