Content-based retrieval from digital video

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 frameworks 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 & drop queries and authoring of the video retrieval system.