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

Not logged in : Login

(Sponging disallowed)

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Context-aware Mouse Behaviour Recognition using Hidden Markov Models Goto Sponge NotDistinct Permalink

An Entity of Type : bibo:AcademicArticle, within Data Space : linkeddata.uriburner.com:28898 associated with source document(s)

Attributes	Values
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/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/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://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #69761 Context-aware Mouse Behaviour Recognition using Hidden Markov Models

Faceted Search & Find service v1.17_git144 as of Jul 26 2024

Alternative Linked Data Documents: iSPARQL | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3331 as of Aug 25 2024, on Linux (x86_64-ubuntu_noble-linux-glibc2.38-64), Single-Server Edition (378 GB total memory, 37 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software