Sequential Correspondence Hierarchical Dirichlet Processes for Video Data Analysis

Jianfei Xue, Koji Eguchi

Abstract: Multimedia data mining based on topic models as an emerging technique has become a very popular research topic in recent years. In this paper, we propose 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 considered as an extended hierarchical Dirichlet processes (HDP) model containing two important features: one is the time-dependency mechanism that connects neighboring video frames on the basis of a time dependent Markovian assumption, and the other is the data 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. We present a comprehensive evaluation for Seq-cHDP through experimentation and finally demonstrate that our model outperforms than other baseline models.