About: Context-aware Mouse Behaviour Recognition using Hidden Markov Models

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #69761 Context-aware Mouse Behaviour Recognition using Hidden Markov Models
sameAs	Context-aware Mouse Behaviour Recognition using Hidden Markov Models
http://eprints.org/ontology/hasAccepted	Context-aware Mouse Behaviour Recognition using Hidden Markov Models (PDF)
http://eprints.org/ontology/hasDocument	Context-aware Mouse Behaviour Recognition using Hidden Markov Models (PDF) Context-aware Mouse Behaviour Recognition using Hidden Markov Models (Other) Context-aware Mouse Behaviour Recognition using Hidden Markov Models (Other) Context-aware Mouse Behaviour Recognition using Hidden Markov Models (Other) Context-aware Mouse Behaviour Recognition using Hidden Markov Models (Other) Context-aware Mouse Behaviour Recognition using Hidden Markov Models (Other)
dc:hasVersion	Context-aware Mouse Behaviour Recognition using Hidden Markov Models (PDF)
Title	Context-aware Mouse Behaviour Recognition using Hidden Markov Models
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2018-10-10
Creator	Huiyu Zhou Brian D Green Danny Crookes Shengping Zhang Yunfeng Zhao Zheheng Jiang Haiping Ma Dacheng Tao L. Li
status	peer reviewed published
Publisher	IEEE
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.
Is Part Of	IEEE Transactions on Image Processing https://kar.kent.ac.uk/id/repository
Subject	QA 76 Software, computer programming,
list of authors	https://kar.kent.ac.uk/id/eprint/69761#authors
issue	3
volume	28
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #69761 Context-aware Mouse Behaviour Recognition using Hidden Markov Models

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