Content-based video indexing of TV broadcast news using hidden Markov models

This paper presents a new approach to content-based video indexing using hidden Markov models (HMMs). In this approach one feature vector is calculated for each image of the video sequence. These feature vectors are modeled and classified using HMMs. This approach has many advantages compared to other video indexing approaches. The system has automatic learning capabilities. It is trained by presenting manually indexed video sequences. To improve the system we use a video model, that allows the classification of complex video sequences. The presented approach works three times faster than real-time. We tested our system on TV broadcast news. The rate of 97.3% correctly classified frames shows the efficiency of our system