PT Unknown
AU Mohammad Ali Bagheri
   Qigang Gao
   Sergio Escalera
   Albert Clapes
   Kamal Nasrollahi
   Michael Holte
   Thomas B. Moeslund
TI Keep it Accurate and Diverse: Enhancing Action Recognition Performance by Ensemble Learning
BT IEEE Conference on Computer Vision and Pattern Recognition  Worshops (CVPRW)
PY 2015
BP 22
EP 29
DI 10.1109/CVPRW.2015.7301332
AB The performance of different action recognition techniques has recently been 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 action learning techniques, each performing the recognition task from a different perspective.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 selection 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 performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make useof 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 enhanced performance of the proposed methodology.
ER