An EM Algorithm for Video SummarizationGenerative Model Approach
Abstract
In this paper, we address the visual video summarization problem in a Bayesian framework in order to detect and describe the underlying temporal transformation symmetries in a video sequence. Given a set of time correlated frames, we attempt to extract a reduced number of image-like data structures which are semantically meaningful and that have the ability of representing the sequence evolution. To this end, we present a generative model which involves jointly the representation and the evolution of appearance. Applying Linear Dynamical System theory to this problem, we discuss how the temporal information is encoded yielding a manner of grouping the iconic representations of the video sequence in terms of invariance. The formulation of this problem is driven in terms of a probabilistic approach, which affords a measure of perceptual similarity taking both learned appearance and time evolution models into account.
Cite
Text
Orriols and Binefa. "An EM Algorithm for Video SummarizationGenerative Model Approach." IEEE/CVF International Conference on Computer Vision, 2001. doi:10.1109/ICCV.2001.937645Markdown
[Orriols and Binefa. "An EM Algorithm for Video SummarizationGenerative Model Approach." IEEE/CVF International Conference on Computer Vision, 2001.](https://mlanthology.org/iccv/2001/orriols2001iccv-em/) doi:10.1109/ICCV.2001.937645BibTeX
@inproceedings{orriols2001iccv-em,
title = {{An EM Algorithm for Video SummarizationGenerative Model Approach}},
author = {Orriols, Xavier and Binefa, Xavier},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {2001},
pages = {335-342},
doi = {10.1109/ICCV.2001.937645},
url = {https://mlanthology.org/iccv/2001/orriols2001iccv-em/}
}