4D Effect Video Classification with Shot-Aware Frame Selection and Deep Neural Networks
Abstract
A 4D effect video played at cinema or other designated places is a video annotated with physical effects such as motion, vibration, wind, flashlight, water spray, and scent. In order to automate the time-consuming and labor-intensive process of creating such videos, we propose a new method to classify videos into 4D effect types with shot-aware frame selection and deep neural networks (DNNs). Shot-aware frame selection is a process of selecting video frames across multiple shots based on the shot length ratios to subsample every video down to a fixed number of frames for classification. For empirical evaluation, we collect a new dataset of 4D effect videos where most of the videos consist of multiple shots. Our extensive experiments show that the proposed method consistently outperforms DNNs without considering multi-shot aspect by up to 8.8% in terms of mean average precision.
Cite
Text
Siadari et al. "4D Effect Video Classification with Shot-Aware Frame Selection and Deep Neural Networks." IEEE/CVF International Conference on Computer Vision Workshops, 2017. doi:10.1109/ICCVW.2017.139Markdown
[Siadari et al. "4D Effect Video Classification with Shot-Aware Frame Selection and Deep Neural Networks." IEEE/CVF International Conference on Computer Vision Workshops, 2017.](https://mlanthology.org/iccvw/2017/siadari2017iccvw-4d/) doi:10.1109/ICCVW.2017.139BibTeX
@inproceedings{siadari2017iccvw-4d,
title = {{4D Effect Video Classification with Shot-Aware Frame Selection and Deep Neural Networks}},
author = {Siadari, Thomhert S. and Han, Mikyong and Yoon, Hyunjin},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2017},
pages = {1148-1155},
doi = {10.1109/ICCVW.2017.139},
url = {https://mlanthology.org/iccvw/2017/siadari2017iccvw-4d/}
}