Content-Based Video Retrieval: A Comprehensive Review of Methods, Frameworks, and Trends

Abstract

Multimedia retrieval and delivery play a critical role in numerous application domains, addressing the challenge of identifying, filtering, and managing exponentially growing volumes of multimedia data. Retrieval is the process by which a user or system identifies needed content, prompting a retrieval system to return relevant data. Dynamic videos possess distinct characteristics that differ from static images, containing content structured across shot, scene, and program levels. Effectively retrieving such content requires an automatic system capable of operating at each of these levels. Content-Based Video Retrieval (CBVR) is the process of efficiently searching for and retrieving video content while ensuring high relevance of the results. This review provides a tutorial and a comprehensive overview of the techniques used for content-based video retrieval. It focuses on video retrieval methodologies alongside an overview of video indexing. A generic CBVR framework is discussed in detail. The core CBVR section includes a literature review and discussion to explore prevailing trends in retrieval systems. The TRECVID benchmark for evaluating different retrieval systems is analyzed. Furthermore, key techniques such as similarity measures and relevance feedback for video retrieval are examined. Finally, future research directions are identified and discussed.