Content Based Visual Information Retrieval using Deep Learning

Abstract

Content-based visual information retrieval (CBVIR) is a multidisciplinary area of research that focuses on using visual content such as images and videos for efficient and relevant information retrieval from large databases. Traditional text-based search techniques rely on metadata like keywords or tags, but CBVIR uses features extracted directly from the content itself—such as colors, textures, shapes, and patterns—allowing for more flexible and accurate searches. This paper reviews key advancements in CBVIR, including its techniques, challenges, and applications, and provides an overview of current research and trends in the field.