TY  - CONF
AU  - Mohammad Ali Bagheri
AU  - Gang Hu
AU  - Qigang Gao
AU  - Sergio Escalera
A2  - ICPR
PY  - 2014//
TI  - A Framework of Multi-Classifier Fusion for Human Action Recognition
BT  - 22nd International Conference on Pattern Recognition
SP  - 1260
EP  - 1265
N2  - The performance of different action-recognition methods using skeleton joint locations have been recently studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of five action learning techniques, each performing the recognition task from a different perspective. The underlying rationale of the fusion approach is that different learners employ varying structures of input descriptors/features to be trained. These varying structures cannot be attached and used by a single learner. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a poorly performing learner. This leads to having a more robust and general-applicable framework. Also, we propose two simple, yet effective, action description techniques. In order to improve the recognition performance, 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 classifiers are compared with those obtained from fusing the classifiers' output, showing advanced performance of the proposed methodology.
SN  - 1051-4651
L1  - http://refbase.cvc.uab.es/files/BHG2014.pdf
UR  - http://dx.doi.org/10.1109/ICPR.2014.226
N1  - HuPBA;MILAB
ID  - Mohammad Ali Bagheri2014
ER  -