Embedding based on function approximation for large scale image search
- Submitting institution
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The University of Liverpool
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 12151
- Type
- D - Journal article
- DOI
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10.1109/TPAMI.2017.2686861
- Title of journal
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Article number
- -
- First page
- 626
- Volume
- 40
- Issue
- 3
- ISSN
- 0162-8828
- Open access status
- Technical exception
- Month of publication
- March
- Year of publication
- 2017
- 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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1
- Research group(s)
-
-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A preliminary version of this paper with the title "FAemb: a function approximation-based embedding method for image retrieval" was published at CVPR 2015. The techniques developed in this paper directly led to follow-up research, including by Do and co-authors in "Selective Deep Convolutional Features for Image Retrieval" (ACM MM 2017) and "Simultaneous Feature Aggregating and Hashing for Compact Binary Code Learning" (IEEE TIP 2019), both not REF returned.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -