Exploring encoding and normalization methods on probabilistic latent semantic analysis model for action recognition

Topic models have been wildly applied in the field of computer vision, through which superior performance was yielded in various recognizing tasks. Among them, probabilistic latent semantic analysis model has earned much attention due to its simplicity and effect. But the affection of encoding and normalization methods on topic models has been ignored during the period. 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. The recognition accuracy reachs 96.44% and 93.33% 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.