dc.contributor.author |
Burke, Michael G
|
|
dc.date.accessioned |
2017-06-07T07:10:37Z |
|
dc.date.available |
2017-06-07T07:10:37Z |
|
dc.date.issued |
2016-12 |
|
dc.identifier.citation |
Burke, M. 2016. Image ranking in video sequences using pairwise image comparisons and temporal smoothing. 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 30 November-2 December 2016, Stellenbosch, Cape Town. DOI: 10.1109/RoboMech.2016.7813166 |
en_US |
dc.identifier.isbn |
978-1-5090-3335-5 |
|
dc.identifier.uri |
DOI: 10.1109/RoboMech.2016.7813166
|
|
dc.identifier.uri |
http://ieeexplore.ieee.org/document/7813166/
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/9174
|
|
dc.description |
2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 30 November-2 December 2016, Stellenbosch, Cape Town. |
en_US |
dc.description.abstract |
The ability to predict the importance of an image is highly desirable in computer vision. This work introduces an image ranking scheme suitable for use in video or image sequences. Pairwise image comparisons are used to determine image ‘interest’ values within a standard Bayesian ranking framework, and a Rauch-Tung-Striebel smoother is used to improve these interest scores. Results show that the training data requirements typically associated with pairwise ranking systems are dramatically reduced by incorporating temporal smoothness constraints. Experiments on a coastal image dataset show that smoothed pairwise ranking can provide ranking results equivalent to standard pairwise ranking with less than half the training data. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Worklist;17988 |
|
dc.subject |
Image ranking |
en_US |
dc.subject |
Bayesian modelling |
en_US |
dc.subject |
Interest detection |
en_US |
dc.title |
Image ranking in video sequences using pairwise image comparisons and temporal smoothing |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Burke, M. G. (2016). Image ranking in video sequences using pairwise image comparisons and temporal smoothing. IEEE. http://hdl.handle.net/10204/9174 |
en_ZA |
dc.identifier.chicagocitation |
Burke, Michael G. "Image ranking in video sequences using pairwise image comparisons and temporal smoothing." (2016): http://hdl.handle.net/10204/9174 |
en_ZA |
dc.identifier.vancouvercitation |
Burke MG, Image ranking in video sequences using pairwise image comparisons and temporal smoothing; IEEE; 2016. http://hdl.handle.net/10204/9174 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Burke, Michael G
AB - The ability to predict the importance of an image is highly desirable in computer vision. This work introduces an image ranking scheme suitable for use in video or image sequences. Pairwise image comparisons are used to determine image ‘interest’ values within a standard Bayesian ranking framework, and a Rauch-Tung-Striebel smoother is used to improve these interest scores. Results show that the training data requirements typically associated with pairwise ranking systems are dramatically reduced by incorporating temporal smoothness constraints. Experiments on a coastal image dataset show that smoothed pairwise ranking can provide ranking results equivalent to standard pairwise ranking with less than half the training data.
DA - 2016-12
DB - ResearchSpace
DP - CSIR
KW - Image ranking
KW - Bayesian modelling
KW - Interest detection
LK - https://researchspace.csir.co.za
PY - 2016
SM - 978-1-5090-3335-5
T1 - Image ranking in video sequences using pairwise image comparisons and temporal smoothing
TI - Image ranking in video sequences using pairwise image comparisons and temporal smoothing
UR - http://hdl.handle.net/10204/9174
ER -
|
en_ZA |