Principal Video Shot: Linking Low-Level Perceptional Features to Semantic Video Events
Abstract
In this paper, we propose a novel framework for semantic medical event characterization and detection by using principal video shots and semantic principal video shot classification. Specifically, the framework includes: (a) A semantic medical event characterization technique by using principal video shots in a specific surgery education video domain. (b) An automatic principal video shot detection algorithm by determining the domain-dependent and event-driven salient objects. (c) A semantic medical event detection technique by using Bayesian classifier, where the classifier parameters and structure are determined automatically by an adaptive Expectation-Maximization (EM) algorithm. For semantic medical event detection in a specific surgery education video domain, our technique achieves overall \approx 87:3% accuracy for four pre-defined semantic medical events.
Cite
Text
Fan and Luo. "Principal Video Shot: Linking Low-Level Perceptional Features to Semantic Video Events." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003. doi:10.1109/CVPRW.2003.10033Markdown
[Fan and Luo. "Principal Video Shot: Linking Low-Level Perceptional Features to Semantic Video Events." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003.](https://mlanthology.org/cvprw/2003/fan2003cvprw-principal/) doi:10.1109/CVPRW.2003.10033BibTeX
@inproceedings{fan2003cvprw-principal,
title = {{Principal Video Shot: Linking Low-Level Perceptional Features to Semantic Video Events}},
author = {Fan, Jianping and Luo, Hangzai},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2003},
pages = {37},
doi = {10.1109/CVPRW.2003.10033},
url = {https://mlanthology.org/cvprw/2003/fan2003cvprw-principal/}
}