Exploring encoding and normalization methods on probabilistic latent semantic analysis model for action recognition
| Autor: | Zhenyang Wu, Tongchi Zhou, Qinjun Xu, Lin Zhou |
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| Rok vydání: | 2016 |
| Předmět: |
Topic model
Normalization (statistics) Probabilistic latent semantic analysis business.industry Computer science Pattern recognition 02 engineering and technology Machine learning computer.software_genre Activity recognition 0202 electrical engineering electronic engineering information engineering Action recognition 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | WCSP |
| DOI: | 10.1109/wcsp.2016.7752504 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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