Abstract
The performance of different action recognition tech- niques has recently been studied by several computer vision researchers. However, the potential improvement in classi- fication through classifier fusion by ensemble-based meth- ods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different per- spective. The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple and diverse classifiers, each trained with different feature set. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selec- tion of a learner on an unseen action recognition scenario. This leads to having a more robust and general-applicable framework. In order to improve the recognition perfor- mance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual clas- sifiers are compared with those obtained from fusing the classifiers’ output, showing enhanced performance of the proposed methodology.
Originalsprog | Engelsk |
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Titel | IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), 2015 |
Forlag | IEEE Computer Society Press |
Publikationsdato | 7 jun. 2015 |
Sider | 22-29 |
ISBN (Trykt) | 978-1-4673-6759-2 |
DOI | |
Status | Udgivet - 7 jun. 2015 |
Begivenhed | 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshop: Looking at People - Boston, USA Varighed: 6 jun. 2015 → 12 jun. 2015 |
Konference
Konference | 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshop |
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Land/Område | USA |
By | Boston |
Periode | 06/06/2015 → 12/06/2015 |