概率隐含语义分析模型在行为识别中的编码与归一化方法研究

在视频中的行为识别的语境下，为了提高概率隐含语义分析模型的识别性能，研究了不同编码方法结合归一化方法对于分类性能的影响；还考察了主成分分析预处理原始特征对于性能的影响，在显著降低特征维度进而降低计算量的同时，当特征包含较多噪声成分的情况下性能甚至会有所提升。在KTH和UT-interaction 数据库上的实验表明，编码和归一化方法的适当组合可以显著提高模型的性能。在UT-interaction数据库的两个子集上识别精度分别达到了当前最好的结果96.44%、95%，其中在数据集1上采用稀疏的时空兴趣点特征，得到了94.24%的识别精度。 

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.