Unsupervised Spectral Dual Assignment Clustering of Human Actions in Context

Simon Jones, Ling Shao

Research output: Contribution to conferencePaper

34 Citations (Scopus)

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.
Original languageEnglish
DOIs
Publication statusPublished - Jun 2014
EventCVPR 2014 - IEEE Conference on Computer Vision and Pattern Recognition - Columbus, Ohio
Duration: 1 Jun 2014 → …

Conference

ConferenceCVPR 2014 - IEEE Conference on Computer Vision and Pattern Recognition
Period1/06/14 → …

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