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Multi-View Mouse Social Behaviour Recognition With Deep Graphic Model

Jiang, Zheheng, Zhou, Feixiang, Zhao, Aite, Li, Xin, Li, Ling, Tao, Dacheng, Li, Xuelong, Zhou, Huiyu (2021) Multi-View Mouse Social Behaviour Recognition With Deep Graphic Model. IEEE Transactions on Image Processing, 30 . pp. 5490-5504. ISSN 1057-7149. E-ISSN 1941-0042. (doi:10.1109/TIP.2021.3083079) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:93723)

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Home-cage social behaviour analysis of mice is an invaluable tool to assess therapeutic efficacy of neurodegenerative diseases. Despite tremendous efforts made within the research community, single-camera video recordings are mainly used for such analysis. Because of the potential to create rich descriptions for mouse social behaviors, the use of multi-view video recordings for rodent observations is increasingly receiving much attention. However, identifying social behaviours from various views is still challenging due to the lack of correspondence across data sources. To address this problem, we here propose a novel multi-view latent-attention and dynamic discriminative model that jointly learns view-specific and view-shared sub-structures, where the former captures unique dynamics of each view whilst the latter encodes the interaction between the views. Furthermore, a novel multi-view latent-attention variational autoencoder model is introduced in learning the acquired features, enabling us to learn discriminative features in each view. Experimental results on the standard CRMI13 and our multi-view Parkinson's Disease Mouse Behaviour (PDMB) datasets demonstrate that our proposed model outperforms the other state of the arts technologies, has lower computational cost than the other graphical models and effectively deals with the imbalanced data problem.

Item Type: Article
DOI/Identification number: 10.1109/TIP.2021.3083079
Subjects: Q Science > Q Science (General)
Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Caroline Li
Date Deposited: 24 Mar 2022 23:54 UTC
Last Modified: 25 Mar 2022 09:37 UTC
Resource URI: (The current URI for this page, for reference purposes)
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