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Source

Information processing and management. 36(2000) no.1, S.87-94

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Abstract

Video is a communications medium that normally brings together moving pictures with a synchronized audio track into a discrete piece or pieces of information. A "piece" of video is variously referred to as a frame, a shot, a scene, a Clip, a program, or an episode; these pieces are distinguished by their length and by their composition. We shall return to the definition of each of these in the section an automatically structuring and indexing digital video. In modern society, Video is commonplace and is usually equated with television, movies, or home Video produced by a Video camera or camcorder. We also accept Video recorded from closed circuit TVs for security and surveillance as part of our daily lives. In short, Video is ubiquitous. Digital Video is, as the name suggests, the creation or capture of Video information in digital format. Most Video produced today, commercial, surveillance, or domestic, is produced in digital form, although the medium of Video predates the development of digital computing by several decades. The essential nature of Video has not changed with the advent of digital computing. It is still moving pictures and synchronized audio. However, the production methods and the end product have gone through significant evolution, in the last decade especially.

Source

Annual review of information science and technology. 38(2004), S.371-409

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Abstract

The TREC benchmarking exercise for information retrieval (IR) experiments has provided a forum and an opportunity for IR researchers to evaluate the performance of their approaches to the IR task and has resulted in improvements in IR effectiveness. Typically, retrieval performance has been measured in terms of precision and recall, and comparisons between different IR approaches have been based on these measures. These measures are in turn dependent on the so-called "pool depth" used to discover relevant documents. Whereas there is evidence to suggest that the pool depth size used for TREC evaluations adequately identifies the relevant documents in the entire test data collection, we consider how it affects the evaluations of individual systems. The data used comes from the Sixth TREC conference, TREC-6. By fitting appropriate regression models we explore whether different pool depths confer advantages or disadvantages on different retrieval systems when they are compared. As a consequence of this model fitting, a pair of measures for each retrieval run, which are related to precision and recall, emerge. For each system, these give an extrapolation for the number of relevant documents the system would have been deemed to have retrieved if an indefinitely large pool size had been used, and also a measure of the sensitivity of each system to pool size. We concur that even on the basis of analyses of individual systems, the pool depth of 100 used by TREC is adequate

Source

Journal of the American Society for Information Science and technology. 52(2001) no.7, S.570-574