Image retrieval based on query by saliency content
- Submitting institution
-
University of York
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 54874506
- Type
- D - Journal article
- DOI
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10.1016/j.dsp.2014.09.005
- Title of journal
- Digital Signal Processing
- Article number
- -
- First page
- 156
- Volume
- 36
- Issue
- -
- ISSN
- 1051-2004
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- Year of publication
- 2014
- 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
-
1
- Research group(s)
-
-
- Citation count
- 34
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is among the first that uses visual attention mechanisms for content based image retrieval (CBIR). The visual attention is a computational model of human visual properties. The visual attention mechanism is used in conjunction with various local and global features in order to maximize the CBIR result. The proposed method was rigorously applied on several image databases and influenced several other more recent research studies in the area of image retrieval based on query by saliency content (Zhang et al Tonji University, Wei et al Beijing Jiaotong).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -