Graph-based Shot Type Classification in Large Historical Film Archives

Daniel Helm, Florian Kleber, Martin Kampel

2022

Abstract

To analyze films and documentaries (indexing, content understanding), a shot type classification is needed. State-of-the-art approaches use traditional CNN-based methods, which need large datasets for training (CineScale with 792000 frames or MovieShots with 46K shots). To overcome this problem, a Graph-based Shot TypeClassifier (GSTC) is proposed, which is able to classify shots into the following types: Extreme-Long-Shot (ELS), Long-Shot (LS), Medium-Shot (MS), Close-Up (CU), Intertitle (I), and Not Available/Not Clear (NA). The methodology is evaluated on standard datasets as well as a new published dataset: HistShotDS-Ext, including 25000 frames. The proposed Graph-based Shot Type Classifier reaches a classification accuracy of 86%.

Paper Citation

in Harvard Style

Helm D., Kleber F. and Kampel M. (2022). Graph-based Shot Type Classification in Large Historical Film Archives. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP; ISBN 978-989-758-555-5, SciTePress, pages 991-998. DOI: 10.5220/0010905800003124

in Bibtex Style

@conference{visapp22,
author={Daniel Helm and Florian Kleber and Martin Kampel},
title={Graph-based Shot Type Classification in Large Historical Film Archives},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={991-998},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010905800003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP
TI - Graph-based Shot Type Classification in Large Historical Film Archives
SN - 978-989-758-555-5
AU - Helm D.
AU - Kleber F.
AU - Kampel M.
PY - 2022
SP - 991
EP - 998
DO - 10.5220/0010905800003124
PB - SciTePress