Cross-view Retrieval via Probability-based Semantics-Preserving Hashing
- Submitting institution
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University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 27499304
- Type
- D - Journal article
- DOI
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10.1109/TCYB.2016.2608906
- Title of journal
- IEEE Transactions on Cybernetics
- Article number
- -
- First page
- 4342
- Volume
- 47
- Issue
- 12
- ISSN
- 2168-2267
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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D - Computer Vision and Natural Computing (CVNC)
- Citation count
- 76
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research work was supported by the National Natural Science Foundation of China under Grant 61571269 and Grant 61271394. This research is about efficiently retrieving nearest neighbours from large-scale Multiview data which can substantially improve query speeds. This research proposes an effective probability-based semantics preserving hashing (SePH) method to tackle the problem of cross-view retrieval. It is a novel probabilistic approach which is proposed to utilize it for determining a unified hash code. Experiments benchmark datasets showed that SePH yielded the state-of-the-art performance for cross-view retrieval.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -