Foundations of Content-Based Video Retrieval

As video data grows exponentially, traditional text-based search is no longer enough to find specific moments or visual patterns. Understanding how algorithms analyze, index, and retrieve video files based on their actual visual and auditory content is a crucial skill for modern computer science and information retrieval. This text-only course guides you through the foundational concepts of Content-Based Video Retrieval (CBVR), progressing from basic terminology and feature extraction to modern vector-based search architectures. What you'll learn: - Understand the core architecture of content-based video retrieval systems. - Analyze keyframes, shot boundary detection, and temporal video segmentation. - Extract visual features using color histograms, texture analysis, and modern deep learning models. - Apply vector databases and similarity metrics to perform high-speed video search. - Evaluate retrieval performance using precision, recall, and mean average precision (mAP). - Explore multi-modal retrieval techniques combining text, audio, and visual data. The course begins with foundational definitions of video structure and feature representation, then progresses to indexing strategies, modern search pipelines, and evaluation frameworks through clear written explanations and conceptual exercises. It is designed for computer science students, software engineers, and IT professionals looking to build a strong foundation in video processing and retrieval, with no advanced prerequisites required. Start reading today to master the mechanics of modern video search systems.