Research on encoding and normalizing methods in probabilistic latent semantic analysis model for action recognition

 In order to improve the classifying accuracy, this paper explores the impact of encoding methods combined with different normalization on probabilistic latent semantic analysis model in the context of action classification in videos. Detailed experiments are conducted on KTH and UT-interaction datasets. The results show that an appropriate combination of encoding and normalization methods could significantly improve the performance of probabilistic latent semantic analysis model. Then preprocessing of raw features using principle component analysis is investigated, through which，while the dimension of features and computing quantity are reduced，the performance is even improved when raw features contain considerable noises. The recognition accuracy reaches 96.44% and 95% on UT-interaction set1 and set2 respectively, which outperforms the state-of-the-art. Especially, we obtain 94.24% on UT-interaction set1 using sparse STIPs.