Content-based retrieval from digital video

摘要

There is already a huge demand for efficient image indexing and content-based retrieval. With TV going digital, advances in real-time video decompression, easy access to the Internet and the availability of cheap mass storage and fast graphics adaptor cards, digital video will become the next `big' media. Unfortunately, automatic indexing and feature extraction from digital video is even harder than still-image analysis. Presently, automatic analysis of digital video is mostly restricted to automatic detection of scene changes. In this paper we present a framework suitable to immediately explore the consequences of content-based video retrieval with a high granularity of video content. The framework employs Semantic networks to represent video contents on a high level of abstraction and uses time-varying sensitive regions to link objects in a video to the knowledge base. A prototype was implemented under NEXTSTEP, exploiting the rich user-interface capabilities of this platform to feature drag and drop queries and authoring of the video retrieval system.