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Visual Form 2001

Martin-Granel Pierre, Roach Matthew, Mason John, Matt Roach Orcid Logo

Lecture notes in computer science

Swansea University Author: Matt Roach Orcid Logo

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DOI (Published version): 10.1007/3-540-45129-3

Abstract

This paper considers camera motion extraction with application to automatic video classification. Video motion is subdivided into 3 components, one of which, camera motion, is considered here. The extraction of the camera motion is based on correlation. Both subjective and objective measures of the...

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Published in: Lecture notes in computer science
ISBN: 978-3-540-42120-7 978-3-540-45129-7
Published: 2001
URI: https://cronfa.swan.ac.uk/Record/cronfa39133
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first_indexed 2018-03-22T05:12:36Z
last_indexed 2018-03-22T05:12:36Z
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spelling 2018-03-21T20:19:45.3853079 v2 39133 2018-03-21 Visual Form 2001 9722c301d5bbdc96e967cdc629290fec 0000-0002-1486-5537 Matt Roach Matt Roach true false 2018-03-21 SCS This paper considers camera motion extraction with application to automatic video classification. Video motion is subdivided into 3 components, one of which, camera motion, is considered here. The extraction of the camera motion is based on correlation. Both subjective and objective measures of the performance of the camera motion extraction are presented. This approach is shown to be simple but efficient and effective. This form is separated and extracted as a discriminant for video classification. In a simple classification experiment it is shown that sport and non-sport videos can be classified with an identification rate of 80%. The system is shown to be able to verify the genre of a short sequence (only 12 seconds), for sport and non-sport, with a false acceptance rate of 10% on arbitrarily chosen test sequences. Journal Article Lecture notes in computer science 978-3-540-42120-7 978-3-540-45129-7 31 5 2001 2001-05-31 10.1007/3-540-45129-3 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2018-03-21T20:19:45.3853079 2018-03-21T20:19:45.1824859 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Martin-Granel Pierre 1 Roach Matthew 2 Mason John 3 Matt Roach 0000-0002-1486-5537 4
title Visual Form 2001
spellingShingle Visual Form 2001
Matt Roach
title_short Visual Form 2001
title_full Visual Form 2001
title_fullStr Visual Form 2001
title_full_unstemmed Visual Form 2001
title_sort Visual Form 2001
author_id_str_mv 9722c301d5bbdc96e967cdc629290fec
author_id_fullname_str_mv 9722c301d5bbdc96e967cdc629290fec_***_Matt Roach
author Matt Roach
author2 Martin-Granel Pierre
Roach Matthew
Mason John
Matt Roach
format Journal article
container_title Lecture notes in computer science
publishDate 2001
institution Swansea University
isbn 978-3-540-42120-7
978-3-540-45129-7
doi_str_mv 10.1007/3-540-45129-3
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
active_str 0
description This paper considers camera motion extraction with application to automatic video classification. Video motion is subdivided into 3 components, one of which, camera motion, is considered here. The extraction of the camera motion is based on correlation. Both subjective and objective measures of the performance of the camera motion extraction are presented. This approach is shown to be simple but efficient and effective. This form is separated and extracted as a discriminant for video classification. In a simple classification experiment it is shown that sport and non-sport videos can be classified with an identification rate of 80%. The system is shown to be able to verify the genre of a short sequence (only 12 seconds), for sport and non-sport, with a false acceptance rate of 10% on arbitrarily chosen test sequences.
published_date 2001-05-31T03:49:40Z
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score 10.999161

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