Context-Aware Mouse Behavior Recognition Using Hidden Markov Models
- Submitting institution
-
The University of Leicester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1429
- 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
-
-
- Supplementary information
-
https://doi.org/10.1109/TIP.2018.2875335
- 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
-
8
- Research group(s)
-
-
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Studying neurobehavioral phenotypes can be of great interest because the first symptom of neurodegenerative disorders is often identifiable through subtle changes in daily mouse behaviors. This paper proposes to establish a novel segment aggregate network for mouse behavior recognition. Part of our technology has been used by different disciplines through publicly accessible source code and a new database (e.g. Han et al. Food Control, 2019; van Dam et al. Journal of Neuroscience Methods, 2020; Zhang et al. ACM TOMM, 2020). A preliminary version of this paper was shortlisted for “Best Student Paper Award” of ICPRAM 2017.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -