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High-level feature detection from video in TRECVid: a 5-year retrospective of achievements

Smeaton, Alan F. and Over, Paul and Kraaij, Wessel (2009) High-level feature detection from video in TRECVid: a 5-year retrospective of achievements. In: Divakaran, Ajay, (ed.) Multimedia Content Analysis: Theory and Applications. Springer series on Signals and Communication Technology . Springer US, Norwell, MA 02061, USA, pp. 151-174. ISBN 978-0-387-76567-9

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Successful and effective content-based access to digital video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip. The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work done on the TRECVid high-level feature task, showing the progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can achieve large-scale, fast and reliable high-level feature detection on video.

Item Type:Book Section
Uncontrolled Keywords:Semantic Concepts; video indexing;
Subjects:Computer Science > Multimedia systems
Computer Science > Image processing
Computer Science > Digital video
DCU Faculties and Centres:Research Initiatives and Centres > Centre for Digital Video Processing (CDVP)
Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Publisher:Springer US
Official URL:
Copyright Information:© Springer 2009
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland
ID Code:2230
Deposited On:06 Jan 2009 11:33 by Alan Smeaton. Last Modified 05 Mar 2009 13:38

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