Video segmentation using minimum ratio similarity measurement

Video segmentation plays an essential role in digital video processing, pattern recognition, security, video conferencing, etc. The convenience of the video is based on its content which is still impossible. One major challenging task of automatic video indexing is automatic detection of video shots. In this paper, a new algorithm is proposed to detect the shot boundary by using the minimum ratio similarity measurement between the characteristic features of two consecutive frames. Where, diverse parameters are calculated for each frame that creates a feature vector of size 40. The system performance is measured in terms of metric parameters. Also, a comparative study with alternative algorithms such as rapid cut detection, histogram-based method, etc. is done. Results suggest that the precision performance of the algorithm is independent of the nature of the video. The F-measure performance comparison shows that the proposed algorithm is the best with maximum average value and minimum standard deviation.