Context-Aware Mouse Behavior Recognition Using Hidden Markov Models
- Submitting institution
-
The University of Kent
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13966
- Type
- D - Journal article
- DOI
-
10.1109/TIP.2018.2875335
- Title of journal
- IEEE Transactions on Image Processing
- Article number
- -
- First page
- 1133
- Volume
- 28
- Issue
- 3
- ISSN
- 1057-7149
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2018
- URL
-
https://kar.kent.ac.uk/69761/
- 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
- Yes
- Number of additional authors
-
8
- Research group(s)
-
-
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Automated recognition of mouse behaviours is crucial in studying psychiatric and neurologic diseases. This paper is significant because it describes a novel Hidden Markov Model (HMM) algorithm to describe the temporal characteristics of mouse behaviours. The architecture is able to discriminate between visually similar behaviours and results in high recognition rates with the strength of processing imbalanced mouse behaviour datasets. The results show that our method outperforms other state-of-the-art approaches.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
