期刊名称：International Journal of Multimedia and Ubiquitous Engineering
印刷版ISSN：1975-0080
出版年度：2016
卷号：11
期号：8
页码：225-236
出版社：SERSC
摘要：A novel feature representation method based on AdaBoost algorithm is put forward for action recognition in this paper. The method can not only adequately describe action in complex scenarios, but also select the most discriminative sample subset from a large amount of raw features of training data. So it can realize a double result, that is, reduce the recognition computational complexity and achieve a good recognition accuracy. The pyramid histogram of oriented gradient feature (PHOG) descriptor is utilized to represent raw feature data. In order to select most discriminative samples subset, AdaBoost algorithm is used to extract the raw feature data. The nearest neighbor classifier algorithm is utilized to test the proposed method on the UCF Sports database. Experiment results show that the method not only achieve the better recognition rate but also greatly improve the speed of recognition
