Bayesian Video Shot Segmentation
Abstract
Prior knowledge about video structure can be used both as a means to improve the peiformance of content analysis and to extract features that allow semantic classification. We introduce statistical models for two important components of this structure, shot duration and activity, and demonstrate the usefulness of these models by introducing a Bayesian formulation for the shot segmentation problem. The new formulations is shown to extend standard thresholding methods in an adaptive and intuitive way, leading to improved segmentation accuracy.
Cite
Text
Vasconcelos and Lippman. "Bayesian Video Shot Segmentation." Neural Information Processing Systems, 2000.Markdown
[Vasconcelos and Lippman. "Bayesian Video Shot Segmentation." Neural Information Processing Systems, 2000.](https://mlanthology.org/neurips/2000/vasconcelos2000neurips-bayesian/)BibTeX
@inproceedings{vasconcelos2000neurips-bayesian,
title = {{Bayesian Video Shot Segmentation}},
author = {Vasconcelos, Nuno and Lippman, Andrew},
booktitle = {Neural Information Processing Systems},
year = {2000},
pages = {1009-1015},
url = {https://mlanthology.org/neurips/2000/vasconcelos2000neurips-bayesian/}
}