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Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop

Philip A Legg, David H. S Chung, Matthew L Parry, Rhodri Bown, Mark Jones Orcid Logo, Iwan W Griffiths, Min Chen

IEEE Transactions on Visualization and Computer Graphics, Volume: 19, Issue: 12, Pages: 2109 - 2118

Swansea University Author: Mark Jones Orcid Logo

DOI (Published version): 10.1109/TVCG.2013.207

Abstract

Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the...

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Published in: IEEE Transactions on Visualization and Computer Graphics
Published: 2013
URI: https://cronfa.swan.ac.uk/Record/cronfa16829
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first_indexed 2014-01-30T17:01:10Z
last_indexed 2019-06-21T13:24:44Z
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spelling 2019-06-21T13:02:05.1385895 v2 16829 2014-01-09 Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 2014-01-09 SCS Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance. Journal Article IEEE Transactions on Visualization and Computer Graphics 19 12 2109 2118 16 10 2013 2013-10-16 10.1109/TVCG.2013.207 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2019-06-21T13:02:05.1385895 2014-01-09T15:22:04.7619923 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Philip A Legg 1 David H. S Chung 2 Matthew L Parry 3 Rhodri Bown 4 Mark Jones 0000-0001-8991-1190 5 Iwan W Griffiths 6 Min Chen 7 0016829-07052015134838.pdf 2013_video__search.pdf 2015-05-07T13:48:38.6430000 Output 13470909 application/pdf Version of Record true 2015-05-07T00:00:00.0000000 true
title Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop
spellingShingle Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop
Mark Jones
title_short Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop
title_full Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop
title_fullStr Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop
title_full_unstemmed Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop
title_sort Transformation of an Uncertain Video Search Pipeline to a Sketch-Based Visual Analytics Loop
author_id_str_mv 2e1030b6e14fc9debd5d5ae7cc335562
author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
author Mark Jones
author2 Philip A Legg
David H. S Chung
Matthew L Parry
Rhodri Bown
Mark Jones
Iwan W Griffiths
Min Chen
format Journal article
container_title IEEE Transactions on Visualization and Computer Graphics
container_volume 19
container_issue 12
container_start_page 2109
publishDate 2013
institution Swansea University
doi_str_mv 10.1109/TVCG.2013.207
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 1
active_str 0
description Traditional sketch-based image or video search systems rely on machine learning concepts as their core technology. However, in many applications, machine learning alone is impractical since videos may not be semantically annotated sufficiently, there may be a lack of suitable training data, and the search requirements of the user may frequently change for different tasks. In this work, we develop a visual analytics systems that overcomes the shortcomings of the traditional approach. We make use of a sketch-based interface to enable users to specify search requirement in a flexible manner without depending on semantic annotation. We employ active machine learning to train different analytical models for different types of search requirements. We use visualization to facilitate knowledge discovery at the different stages of visual analytics. This includes visualizing the parameter space of the trained model, visualizing the search space to support interactive browsing, visualizing candidature search results to support rapid interaction for active learning while minimizing watching videos, and visualizing aggregated information of the search results. We demonstrate the system for searching spatiotemporal attributes from sports video to identify key instances of the team and player performance.
published_date 2013-10-16T03:19:19Z
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score 11.021648

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