David H.S. Chung
Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop
Chung, David H.S.; Legg, Philip A.; Parry, Matthew L.; Jones, Mark W.; Griffiths, Iwan W.; Legg, Philip; Chung, David; Parry, Matthew; Jones, Mark; Griffiths, Iwan; Bown, Rhodri; Chen, Min
Authors
Philip A. Legg
Matthew L. Parry
Mark W. Jones
Iwan W. Griffiths
Professor Phil Legg Phil.Legg@uwe.ac.uk
Professor in Cyber Security
David Chung
Matthew Parry
Mark Jones
Iwan Griffiths
Rhodri Bown
Min Chen
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 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. © 1995-2012 IEEE.
Citation
Griffiths, I. W., Jones, M. W., Parry, M. L., Legg, P. A., Chung, D. H., Legg, P., …Chen, M. (2013). Transformation of an uncertain video search pipeline to a sketch-based visual analytics loop. IEEE Transactions on Visualization and Computer Graphics, 19(12), 2109-2118. https://doi.org/10.1109/TVCG.2013.207
Journal Article Type | Article |
---|---|
Publication Date | Nov 4, 2013 |
Deposit Date | Jun 23, 2015 |
Publicly Available Date | Mar 29, 2024 |
Journal | IEEE Transactions on Visualization and Computer Graphics |
Print ISSN | 1077-2626 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 19 |
Issue | 12 |
Pages | 2109-2118 |
DOI | https://doi.org/10.1109/TVCG.2013.207 |
Keywords | visual knowledge discovery, data clustering, machine learning, multimedia visualization |
Public URL | https://uwe-repository.worktribe.com/output/925488 |
Publisher URL | http://dx.doi.org/10.1109/TVCG.2013.207 |
Additional Information | Additional Information : (c) 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
Files
2013_vast_draft.pdf
(13.7 Mb)
PDF
You might also like
Analyst-driven XAI for time series forecasting: Analytics for telecoms maintenance
(2024)
Conference Proceeding
Improving search space analysis of fuzzing mutators using cryptographic structures
(2023)
Conference Proceeding
Longitudinal risk-based security assessment of docker software container images
(2023)
Journal Article
Teaching offensive and defensive cyber security in schools using a Raspberry Pi Cyber Range
(2023)
Journal Article
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search