Visual information retrieval (VIR) allows users to search, store, and retrieve imagery-based information, such as metadata and visual features, as easily as they would with text documents. This technology extends traditional information retrieval systems beyond text-based queries to handle non-textual information sources like images and videos. VIR systems aim to enable users to query visual content without manual annotation, using advanced techniques from computer vision and database systems. The core challenge in VIR is efficiently and accurately retrieving visual information, which is inherently complex due to the nature of imagery. Traditional text-based search methods often fail to capture the nuances of visual content, leading to inefficiencies. VIR systems address this by leveraging computational methods to extract and analyze visual features, such as color, shape, and texture, enabling more precise and relevant results. VIR systems incorporate various tools and techniques to facilitate visual queries, including image processing, feature-space manipulation, object specification, and measurement tools. These tools help users specify complex queries based on visual characteristics, allowing for more accurate and meaningful results. However, current VIR systems face challenges in balancing the trade-off between minimizing false-negative results and increasing false positives, and in handling the complexity of video content. The development of VIR systems is an ongoing process, with researchers and developers working to improve the effectiveness and efficiency of visual information retrieval. Future advancements in VIR are expected to enhance the ability to cross-reference visual information with other modes of information, making it easier for users to access and utilize visual data in various applications.Visual information retrieval (VIR) allows users to search, store, and retrieve imagery-based information, such as metadata and visual features, as easily as they would with text documents. This technology extends traditional information retrieval systems beyond text-based queries to handle non-textual information sources like images and videos. VIR systems aim to enable users to query visual content without manual annotation, using advanced techniques from computer vision and database systems. The core challenge in VIR is efficiently and accurately retrieving visual information, which is inherently complex due to the nature of imagery. Traditional text-based search methods often fail to capture the nuances of visual content, leading to inefficiencies. VIR systems address this by leveraging computational methods to extract and analyze visual features, such as color, shape, and texture, enabling more precise and relevant results. VIR systems incorporate various tools and techniques to facilitate visual queries, including image processing, feature-space manipulation, object specification, and measurement tools. These tools help users specify complex queries based on visual characteristics, allowing for more accurate and meaningful results. However, current VIR systems face challenges in balancing the trade-off between minimizing false-negative results and increasing false positives, and in handling the complexity of video content. The development of VIR systems is an ongoing process, with researchers and developers working to improve the effectiveness and efficiency of visual information retrieval. Future advancements in VIR are expected to enhance the ability to cross-reference visual information with other modes of information, making it easier for users to access and utilize visual data in various applications.