Classification TV programs based on audio information using hidden Markov model

Abstract

This paper describes a technique for classifying TV broadcast video using a hidden Markov model (HMM). Here we consider the problem of discriminating five types of TV programs, namely commercials, basketball games, football games, news reports, and weather forecasts. Eight frame-based audio features are used to characterize the low-level audio properties, and fourteen clip-based audio features are extracted based on these frame-based features to characterize the high-level audio properties. For each type of these five TV programs, we build an ergodic HMM using the clip-based features as observation vectors. The maximum likelihood method is then used for classifying testing data using the trained models.