Video segmentation using minimum ratio similarity measurement Online publication date: Tue, 23-Jun-2015
by Gautam Pal; Suvojit Acharjee; Dwijen Rudrapaul; Amira S. Ashour; Nilanjan Dey
International Journal of Image Mining (IJIM), Vol. 1, No. 1, 2015
Abstract: 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.
Online publication date: Tue, 23-Jun-2015
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Image Mining (IJIM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org