Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains
- Submitting institution
-
University of Central Lancashire
- Unit of assessment
- 12 - Engineering
- Output identifier
- 19631
- Type
- D - Journal article
- DOI
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10.1016/j.asoc.2017.11.007
- Title of journal
- Applied Soft Computing
- Article number
- -
- First page
- 667
- Volume
- 62
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2018
- 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)
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A - Aerospace and Sensing Group
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces new Machine Learning (ML) clustering techniques to mitigate the shortcomings of existing clustering techniques used in visual domains. The work was conducted in collaboration with the University of Southampton and Gulhane Training and Research Hospital and Medical University (GATA), funded by TUBITAK (≈£25,000). The publication has led to the development of similar techniques citing our new ML clustering technique and our work has been adopted by medical geneticists at GATA to discover new rare sub dysmorphic types that have been previously unclassified in clinical practice.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
