Unsupervised Topic Identification by Integrating Linguistic and Visual Information Based on Hidden Markov Models

Tomohide Shibata, Sadao Kurohashi

Abstract: This paper presents an unsupervised topic identification method integrating linguistic and visual information based on Hidden Markov Models (HMMs). We employ HMMs for topic identification, wherein a state corresponds to a topic and various features including linguistic, visual and audio information are observed. Our experiments on two kinds of cooking TV programs show the effectiveness of our proposed method.