Context-aware Mouse Behaviour Recognition using Hidden Markov Models

Jiang, Zheheng and Crookes, Danny and Green, Brian D and Zhao, Yunfeng and Ma, Haiping and Li, Ling and Zhang, Shengping and Tao, Dacheng and Zhou, Huiyu (2018) Context-aware Mouse Behaviour Recognition using Hidden Markov Models. IEEE Transactions on Image Processing, . ISSN 1057-7149. E-ISSN 1941-0042. (doi:https://doi.org/10.1109/TIP.2018.2875335) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

Abstract

Automated recognition of mouse behaviours is crucial in studying psychiatric and neurologic diseases. To achieve this objective, it is very important to analyse temporal dynamics of mouse behaviours. In particular, the change between mouse neighbouring actions is swift in a short period. In this paper, we develop and implement a novel Hidden Markov Model (HMM) algorithm to describe the temporal characteristics of mouse behaviours. In particular, we here propose a hybrid deep learning architecture, where the first unsupervised layer relies on an advanced spatial-temporal segment Fisher Vector (SFV) encoding both visual and contextual features. Subsequent supervised layers based on our segment aggregate network (SAN) are trained to estimate the state dependent observation probabilities of the HMM. The proposed architecture shows the ability to discriminate between visually similar behaviours and results in high recognition rates with the strength of processing imbalanced mouse behaviour datasets. Finally, we evaluate our approach using JHuang’s and our own datasets, and the results show that our method outperforms other state-of-the-art approaches.

Item Type:	Article
Uncontrolled keywords:	Mouse behaviours, Hidden Markov Model, spatial-temporal segment, Fisher Vector, segment aggregate network
Divisions:	Faculties > Sciences > School of Computing > Computational Intelligence Group Faculties > Sciences > School of Computing > Data Science
Depositing User:	Caroline Li
Date Deposited:	23 Oct 2018 18:48 UTC
Last Modified:	22 Nov 2018 15:32 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/69761 (The current URI for this page, for reference purposes)