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Motion-based classification of cartoons

Roach M., Mason J.S., Pawlewski M., Matt Roach Orcid Logo

Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489)

Swansea University Author: Matt Roach Orcid Logo

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DOI (Published version): 10.1109/ISIMP.2001.925353

Abstract

This paper describes a simple high-level classification of multimedia broadcast material into cartoon non-cartoon. The input video sequences are from a broad range of material which is representative of entertainment viewing. Classification of this type of high-level video genre is difficult because...

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Published in: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489)
Published: 2001
URI: https://cronfa.swan.ac.uk/Record/cronfa39142
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first_indexed 2018-03-22T05:12:38Z
last_indexed 2018-03-22T05:12:38Z
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spelling 2018-03-21T20:19:51.8125774 v2 39142 2018-03-21 Motion-based classification of cartoons 9722c301d5bbdc96e967cdc629290fec 0000-0002-1486-5537 Matt Roach Matt Roach true false 2018-03-21 SCS This paper describes a simple high-level classification of multimedia broadcast material into cartoon non-cartoon. The input video sequences are from a broad range of material which is representative of entertainment viewing. Classification of this type of high-level video genre is difficult because of its large inter-class variation. The task is made more difficult when classification is over a small time (10's of seconds) introducing a great deal of intra-class variation. This paper presents a purely dynamic based approach for content-based classification of video sequences in the form of a new global motion measure of foreground objects. Experiments are reported on a diverse database consisting of: 8 cartoon and 20 non-cartoon sequences. Results are shown in identification error rates against time of sequence used for classification. The system produces a best identification error rate of 3% on 66 separate decisions based on 23 second sequences trained using a total of ~20 minutes of video Other Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489) 31 12 2001 2001-12-31 10.1109/ISIMP.2001.925353 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2018-03-21T20:19:51.8125774 2018-03-21T20:19:51.5785541 Roach M. 1 Mason J.S. 2 Pawlewski M. 3 Matt Roach 0000-0002-1486-5537 4
title Motion-based classification of cartoons
spellingShingle Motion-based classification of cartoons
Matt Roach
title_short Motion-based classification of cartoons
title_full Motion-based classification of cartoons
title_fullStr Motion-based classification of cartoons
title_full_unstemmed Motion-based classification of cartoons
title_sort Motion-based classification of cartoons
author_id_str_mv 9722c301d5bbdc96e967cdc629290fec
author_id_fullname_str_mv 9722c301d5bbdc96e967cdc629290fec_***_Matt Roach
author Matt Roach
author2 Roach M.
Mason J.S.
Pawlewski M.
Matt Roach
format Other
container_title Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing. ISIMP 2001 (IEEE Cat. No.01EX489)
publishDate 2001
institution Swansea University
doi_str_mv 10.1109/ISIMP.2001.925353
document_store_str 0
active_str 0
description This paper describes a simple high-level classification of multimedia broadcast material into cartoon non-cartoon. The input video sequences are from a broad range of material which is representative of entertainment viewing. Classification of this type of high-level video genre is difficult because of its large inter-class variation. The task is made more difficult when classification is over a small time (10's of seconds) introducing a great deal of intra-class variation. This paper presents a purely dynamic based approach for content-based classification of video sequences in the form of a new global motion measure of foreground objects. Experiments are reported on a diverse database consisting of: 8 cartoon and 20 non-cartoon sequences. Results are shown in identification error rates against time of sequence used for classification. The system produces a best identification error rate of 3% on 66 separate decisions based on 23 second sequences trained using a total of ~20 minutes of video
published_date 2001-12-31T03:49:41Z
_version_ 1763752418165653504
score 11.016235

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