Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution to Journal/Magazine › Conference article › peer-review
Research output: Contribution to Journal/Magazine › Conference article › peer-review
}
TY - JOUR
T1 - Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis
AU - Agbele, T.
AU - Ojeme, B.
AU - Jiang, R.
PY - 2019/12/31
Y1 - 2019/12/31
N2 - The paper describes the application of local binary patterns and cascade AdaBoost classifier (CAC) to detect and analyse mice behavioural movement. This was done with a view to investigating the inconsistencies associated with current practices, whereby mice behavioural classification is achieved by means of human-generated labels. The developed cascade AdaBoost algorithm was able to detect eight different mice movement, and we develop a system that allows mice behavioural analysis in videos, with minimal supervision. Evaluating the results on Completeness, Consistency and Correctness, and based on the devised analysis, a solution was deployed, showing that machine learning plays an important role in translating video data into scientific knowledge. This is a useful addition to the animal behaviourist's analytical toolkit.
AB - The paper describes the application of local binary patterns and cascade AdaBoost classifier (CAC) to detect and analyse mice behavioural movement. This was done with a view to investigating the inconsistencies associated with current practices, whereby mice behavioural classification is achieved by means of human-generated labels. The developed cascade AdaBoost algorithm was able to detect eight different mice movement, and we develop a system that allows mice behavioural analysis in videos, with minimal supervision. Evaluating the results on Completeness, Consistency and Correctness, and based on the devised analysis, a solution was deployed, showing that machine learning plays an important role in translating video data into scientific knowledge. This is a useful addition to the animal behaviourist's analytical toolkit.
KW - computer vission
KW - machine learning
KW - mice behavioural pattern detection
KW - local binary patterns
KW - cascade AdaBoost classifier
U2 - 10.1016/j.procs.2019.09.308
DO - 10.1016/j.procs.2019.09.308
M3 - Conference article
VL - 159
SP - 1375
EP - 1386
JO - Procedia Computer Science
JF - Procedia Computer Science
SN - 1877-0509
ER -