Comparison of automatic video segmentation algorithms

While several methods of automatic video segmentation for the identification of shot transitions have been proposed, they have not been systematically compared. We examine several segmentation techniques across different types of videos. Each of these techniques defines a measure of dissimilarity between successive frames which is then compared to a threshold. Dissimilarity values exceeding the threshold identify shot transitions. The techniques are compared in terms of the percentage of correct and false identifications for various thresholds, their sensitivity to the threshold value, their performance across different types of video, their ability to identify complicated transition effects, and their requirements for computational resources. Finally, the definition of a priori set of values for the threshold parameter is also examined. Most techniques can identify over 90% of the real shot transitions but have a high percentage of false positives. Reducing the false positives was a major challenge, and we introduced a local filtering technique that was fairly effective.