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Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis

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Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis. / Agbele, T.; Ojeme, B.; Jiang, R.
In: Procedia Computer Science, Vol. 159, 31.12.2019, p. 1375-1386.

Research output: Contribution to Journal/Magazine › Conference article › peer-review

Harvard

Agbele, T, Ojeme, B & Jiang, R 2019, 'Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis', Procedia Computer Science, vol. 159, pp. 1375-1386. https://doi.org/10.1016/j.procs.2019.09.308

APA

Agbele, T., Ojeme, B., & Jiang, R. (2019). Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis. Procedia Computer Science, 159, 1375-1386. https://doi.org/10.1016/j.procs.2019.09.308

Vancouver

Agbele T, Ojeme B, Jiang R. Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis. Procedia Computer Science. 2019 Dec 31;159:1375-1386. Epub 2019 Oct 14. doi: 10.1016/j.procs.2019.09.308

Author

Agbele, T. ; Ojeme, B. ; Jiang, R. / Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis. In: Procedia Computer Science. 2019 ; Vol. 159. pp. 1375-1386.

Bibtex

@article{1d4eb0f882954d78b542bc8226e4e98d,
title = "Application of local binary patterns and cascade AdaBoost classifier for mice behavioural patterns detection and analysis",
abstract = "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.",
keywords = "computer vission, machine learning, mice behavioural pattern detection, local binary patterns, cascade AdaBoost classifier",
author = "T. Agbele and B. Ojeme and R. Jiang",
year = "2019",
month = dec,
day = "31",
doi = "10.1016/j.procs.2019.09.308",
language = "English",
volume = "159",
pages = "1375--1386",
journal = "Procedia Computer Science",
issn = "1877-0509",
publisher = "Elsevier BV",

}

RIS

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 -