Video shot boundary detection: seven years of TRECVid activity

Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other content-based operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example

So what can we actually do with content-based video retrieval?

In this talk I will give a roller-coaster survey of the state of the art in automatic video analysis, indexing, summarisation, search and browsing as demonstrated in the annual TRECVid benchmarking evaluation campaign. I will concentrate on content-based techniques for video management which form a complement to the dominant paradigm of metadata or tag-based video management and I will use example techniques to illustrate these

Video-4-Video: using video for searching, classifying and summarising video

YouTube has meant that we are now becoming accustomed to searching for video clips, and finding them, for both work and leisure pursuits. But YouTube, like the Internet Archive, OpenVideo and almost everything other video library, doesn't use video to find video, it uses metadata, usually based on user generated content (UGC). But what if we don't know what we're looking for and the metadata doesn't help, or we have poor metadata or no UGC, can we use the video to find video ? Can we automatically derive semantic concepts directly from video which we can use for retrieval or summarisation ? Many dozens of research groups throughout the world work on the problems associated with content-based video search, content-based detection of semantic concepts, shot boundary detection, content-based summarisation and content-based event detection. In this presentation we give a summary of the achievements of almost a decade of research by the TRECVid community, including a report on performance of groups in different TRECVid tasks. We present the modus operandi of the annual TRECVid benchmarking, the problems associated with running an annual evaluation for nearly 100 research groups every year and an overview of the most successful approaches to each task

Applying semantic web technologies to knowledge sharing in aerospace engineering

This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale