Video Data Modeling Using Sequential Correspondence Hierarchical Dirichlet Processes

Jianfei XUE, Koji EGUCHI
2017 IEICE transactions on information and systems  
Jianfei XUE †a) , Nonmember and Koji EGUCHI †b) , Member SUMMARY Video data mining based on topic models as an emerging technique recently has become a very popular research topic. In this paper, we present a novel topic model named sequential correspondence hierarchical Dirichlet processes (Seq-cHDP) to learn the hidden structure within video data. The Seq-cHDP model can be deemed as an extended hierarchical Dirichlet processes (HDP) model containing two important features: one is the
more » ... ndency mechanism that connects neighboring video frames on the basis of a time dependent Markovian assumption, and the other is the correspondence mechanism that provides a solution for dealing with the multimodal data such as the mixture of visual words and speech words extracted from video files. A cascaded Gibbs sampling method is applied for implementing the inference task of Seq-cHDP. We present a comprehensive evaluation for Seq-cHDP through experimentation and finally demonstrate that Seq-cHDP outperforms other baseline models. key words: bayesian nonparametric methods, multimedia machine learning, hierarchical Dirichlet processes, topic models Jianfei Xue is currently pursuing a Ph.D
doi:10.1587/transinf.2016mup0007 fatcat:ige7axxttrantoikabolk4oavm