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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

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Authors

David H.S. Chung

Philip A. Legg

Matthew L. Parry

Mark W. Jones

Iwan W. Griffiths

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.

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