Evaluating Image Retrieval Methods: A Comparative Analysis

Abstract

Image retrieval systems play a crucial role in a variety of applications, from digital asset management to medical imaging and security. This paper presents a comparative analysis of current image retrieval methods, focusing on their effectiveness, efficiency, and applicability across different contexts. We systematically evaluate several prominent techniques, including content-based image retrieval (CBIR), semantic-based retrieval, and hybrid approaches that combine multiple methodologies. The evaluation criteria encompass retrieval accuracy, computational complexity, and user interaction effectiveness. Through a series of experiments and case studies, we assess the performance of these methods using benchmark datasets and real-world scenarios. Our analysis highlights the strengths and limitations of each approach, providing insights into their suitability for specific applications. The findings aim to guide practitioners and researchers in selecting the most appropriate image retrieval method based on their needs and constraints. This study contributes to the understanding of image retrieval technologies and their impact on information retrieval systems.