Citation

Lee, Gin Chong and Loo, Chu Kiong and Liew, Wei Shiung and Wermter, Stefan (2020) Self-Organizing Kernel-based Convolutional Echo State Network for Human Actions Recognition. In: The 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 02.10.2020, Novotel hotel Brugge Centrum, Katelijnestraat 65B, 8000 Brugge (Belgium), Bruges, Belgium.

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Abstract

We propose a deterministic initialization of the Echo State Network reservoirs to ensure that the activation of its internal echo state representations reflects similar topological qualities of the input signal which should lead to a self-organizing reservoir. Human actions encoded as a multivariate time series signal are clustered before using the clustered nodes and interconnectivity matrices for initializing the S-ConvESN reservoirs. The capability of S-ConvESN is evaluated using several 3Dskeleton-based action recognition datasets.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QC Physics > QC 1-75 General
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	06 Oct 2021 02:35
Last Modified:	06 Oct 2021 02:35
URII:	http://shdl.mmu.edu.my/id/eprint/8482

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