ML Anthology

Gradual Transition Detection Using Average Frame Similarity

Timo Volkmer, Seyed M. M. Tahaghoghi, Hugh E. Williams
CVPRW 2004 pp. 139
doi:10.1109/CVPR.2004.357 /cvprw/2004/volkmer2004cvprw-gradual/

Abstract

Segmenting digital video into its constituent basic semantic entities, or shots, is an important step for effective management and retrieval of video data. Recent automated techniques for detecting transitions between shots are highly effective on abrupt transitions. However, automated detection of gradual transitions, and the precise determination of the corresponding start and end frames, remains problematic. In this paper, we present a gradual transition detection approach based on average frame similarity and adaptive thresholds. We report good detection results on the TREC video track collections - particularly for dissolves and fades - and very high accuracy in identifying transition boundaries. Our technique is a valuable new tool for transition detection.

CVPRW Semantic Scholar

Cite

Text

Volkmer et al. "Gradual Transition Detection Using Average Frame Similarity." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2004. doi:10.1109/CVPR.2004.357

Markdown

[Volkmer et al. "Gradual Transition Detection Using Average Frame Similarity." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2004.](https://mlanthology.org/cvprw/2004/volkmer2004cvprw-gradual/) doi:10.1109/CVPR.2004.357

BibTeX

@inproceedings{volkmer2004cvprw-gradual,
  title     = {{Gradual Transition Detection Using Average Frame Similarity}},
  author    = {Volkmer, Timo and Tahaghoghi, Seyed M. M. and Williams, Hugh E.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2004},
  pages     = {139},
  doi       = {10.1109/CVPR.2004.357},
  url       = {https://mlanthology.org/cvprw/2004/volkmer2004cvprw-gradual/}
}