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This paper describes a technique for segmenting video using hidden Markov models (HMM). Video is segmented into regions defined by shots, shot boundaries, and camera movement within shots. Features for segmentation include an image-based distance between adjacent video frames, an audio distance based on the acoustic difference in intervals just before and after the frames, and an estimate of motion between the two frames. Typical video segmentation algorithms classify shot boundaries by computing an image-based distance between adjacent frames and comparing this distance to fixed, manually determined thresholds. Motion and audio information is used separately. In contrast, our segmentation technique allows features to be combined within the HMM framework. Further, thresholds are not required since automatically trained HMMs take their place. This algorithm has been tested on a video data base, and has been shown to improve the accuracy of video segmentation over standard threshold-based systems.
Boreczky et al. (Wed,) studied this question.