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Classification of Non-edited Broadcast Video Using Holistic Low-level Features

Roach Matthew, Xu Li-qun, Mason John, Heath Martlesham, Ip Ipswich, Matt Roach Orcid Logo

Int. Tyrrhenian Workshop on Digital Communications (IWDC)

Swansea University Author: Matt Roach Orcid Logo

Abstract

A data driven learning based approach has been adopted to classify a video programme into a set of five pre-defined genre including a7orts, cartoons, news, commercials and music. The video database studied is a collection of five-hour non-edited TV broadcast programme of dozens of video clips: one h...

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Published in: Int. Tyrrhenian Workshop on Digital Communications (IWDC)
Published: 2002
URI: https://cronfa.swan.ac.uk/Record/cronfa39129
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first_indexed 2018-03-22T05:12:36Z
last_indexed 2018-03-22T05:12:36Z
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spelling 2018-03-21T20:19:42.2652639 v2 39129 2018-03-21 Classification of Non-edited Broadcast Video Using Holistic Low-level Features 9722c301d5bbdc96e967cdc629290fec 0000-0002-1486-5537 Matt Roach Matt Roach true false 2018-03-21 SCS A data driven learning based approach has been adopted to classify a video programme into a set of five pre-defined genre including a7orts, cartoons, news, commercials and music. The video database studied is a collection of five-hour non-edited TV broadcast programme of dozens of video clips: one hour long in total for each genre with an even split of training and testing data. This paper reports the classification result for the two media modes - acoustic and visual - used separately, and for a linear fusion of the two modes at the score level. The correaIonding classification accuracy achieved is approximately 74%, 73%, and 87%, reaIectively, given a 30-second decision window, or for every 30 second non-overlap video segment an answer of its identity is provided Further analysis of the confusion matrices of the classification errors is given. The extension of the approach to the open-set video genre verification and semantic scene segmentation and classification is expected. Other Int. Tyrrhenian Workshop on Digital Communications (IWDC) 31 12 2002 2002-12-31 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2018-03-21T20:19:42.2652639 2018-03-21T20:19:42.2652639 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Roach Matthew 1 Xu Li-qun 2 Mason John 3 Heath Martlesham 4 Ip Ipswich 5 Matt Roach 0000-0002-1486-5537 6
title Classification of Non-edited Broadcast Video Using Holistic Low-level Features
spellingShingle Classification of Non-edited Broadcast Video Using Holistic Low-level Features
Matt Roach
title_short Classification of Non-edited Broadcast Video Using Holistic Low-level Features
title_full Classification of Non-edited Broadcast Video Using Holistic Low-level Features
title_fullStr Classification of Non-edited Broadcast Video Using Holistic Low-level Features
title_full_unstemmed Classification of Non-edited Broadcast Video Using Holistic Low-level Features
title_sort Classification of Non-edited Broadcast Video Using Holistic Low-level Features
author_id_str_mv 9722c301d5bbdc96e967cdc629290fec
author_id_fullname_str_mv 9722c301d5bbdc96e967cdc629290fec_***_Matt Roach
author Matt Roach
author2 Roach Matthew
Xu Li-qun
Mason John
Heath Martlesham
Ip Ipswich
Matt Roach
format Other
container_title Int. Tyrrhenian Workshop on Digital Communications (IWDC)
publishDate 2002
institution Swansea University
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 A data driven learning based approach has been adopted to classify a video programme into a set of five pre-defined genre including a7orts, cartoons, news, commercials and music. The video database studied is a collection of five-hour non-edited TV broadcast programme of dozens of video clips: one hour long in total for each genre with an even split of training and testing data. This paper reports the classification result for the two media modes - acoustic and visual - used separately, and for a linear fusion of the two modes at the score level. The correaIonding classification accuracy achieved is approximately 74%, 73%, and 87%, reaIectively, given a 30-second decision window, or for every 30 second non-overlap video segment an answer of its identity is provided Further analysis of the confusion matrices of the classification errors is given. The extension of the approach to the open-set video genre verification and semantic scene segmentation and classification is expected.
published_date 2002-12-31T03:49:40Z
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score 11.035634

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