Unsupervised Spectral Dual Assignment Clustering of Human Actions in Context

Jones, Simon and Shao, Ling (2014) Unsupervised Spectral Dual Assignment Clustering of Human Actions in Context. In: Proceedings of the CVPR2014. CVF.

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Abstract

A recent trend of research has shown how contextual information related to an action, such as a scene or object, can enhance the accuracy of human action recognition systems. However, using context to improve unsupervised human action clustering has never been considered before, and cannot be achieved using existing clustering methods. To solve this problem, we introduce a novel, general purpose algorithm, Dual Assignment k-Means (DAKM), which is uniquely capable of performing two co-occurring clustering tasks simultaneously, while exploiting the correlation information to enhance both clusterings. Furthermore, we describe a spectral extension of DAKM (SDAKM) for better performance on realistic data. Extensive experiments on synthetic data and on three realistic human action datasets with scene context show that DAKM/SDAKM can significantly outperform the state-of-the-art clustering methods by taking into account the contextual relationship between actions and scenes

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
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Depositing User: Pure Connector
Date Deposited: 10 Feb 2017 02:27
Last Modified: 22 Apr 2020 11:06
URI: https://ueaeprints.uea.ac.uk/id/eprint/62411
DOI:

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