Discriminative Semantic Subspace Analysis for Relevance Feedback
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22062354
- Type
- D - Journal article
- DOI
-
10.1109/TIP.2016.2516947
- Title of journal
- IEEE Transactions on Image Processing
- Article number
- -
- First page
- 1275
- Volume
- 25
- Issue
- 3
- ISSN
- 1057-7149
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- 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
-
2
- Research group(s)
-
D - Computer Vision and Natural Computing (CVNC)
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel idea of subspace learning proposed has enabled better image analysis, which is evidenced by the invited academic seminar in the Hong Kong University of Science and Technology in 2017 (invited by Prof. Chiew-Lan Tai, taicl@cse.ust.hk). Its potential in image retrieval was recognized by Hong et al. in IEEE Transactions on Image Processing, who cite the work as a representative method on fusion-based image retrieval (DOI: 10.1109/TIP.2017.2710635).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
