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: CVPR 2014 - IEEE Conference on Computer Vision and Pattern Recognition, 23rd - 28th June 2014, Columbus, Ohio.

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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:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Human Action Analysis, Unsupervised Learning, Video Clustering
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	16 Jun 2015 09:15
Last Modified:	13 Oct 2019 00:37
URI:	http://nrl.northumbria.ac.uk/id/eprint/22925

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