Lee, Gin Chong and Loo, Chu Kiong and Liew, Wei Shiung (2022) Self-organizing reservoir network for action recognition. Malaysian Journal of Computer Science, 35 (3). pp. 243-263. ISSN 0127-9084, DOI https://doi.org/10.22452/mjcs.vol35no3.4.
Full text not available from this repository.Abstract
Current research in human action recognition (HAR) focuses on efficient and effective modelling of the temporal features of human actions in 3-dimensional space. Echo State Networks (ESNs) are one suitable method for encoding the temporal context due to its short-term memory property. However, the random initialization of the ESN's input and reservoir weights may increase instability and variance in generalization. Inspired by the notion that input-dependent self-organization is decisive for the cortex to adjust the neurons according to the distribution of the inputs, a Self-Organizing Reservoir Network (SORN) is developed based on Adaptive Resonance Theory (ART) and Instantaneous Topological Mapping (ITM) as the clustering process to cater deterministic initialization of the ESN reservoirs in a Convolutional Echo State Network (ConvESN) and yield a Self-Organizing Convolutional Echo State Network (SO-ConvESN). SORN ensures that the activation of ESN???s internal echo state representations reflects similar topological qualities of the input signal which should yield a self-organizing reservoir. In the context of HAR task, human actions encoded as a multivariate time series signals are clustered into clustered node centroids and interconnectivity matrices by SORN for initializing the SO-ConvESN reservoirs. By using several publicly available 3D-skeleton-based action recognition datasets, the impact of vigilance threshold and reservoir perturbation of SORN in performing clustering, the SORN reservoir dynamics and the capability of SO-ConvESN on HAR task have been empirically evaluated and analyzed to produce competitive experimental results.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Echo state networks; Action recognition; Self-organizing networks; Deep neural networks |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 23 Oct 2023 09:01 |
| Last Modified: | 23 Oct 2023 09:01 |
| URI: | http://eprints.um.edu.my/id/eprint/41787 |
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