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Authors: Daniel Helm ; Florian Kleber and Martin Kampel

Affiliation: Computer Vision Lab, Institute of Visual Computing and Human-Centered Technology, TU Wien, Favoritenstraße 9/193-1, Vienna, Austria

Keyword(s): Historical Film Preservation, Film Archives, Deep Learning, Automated Film Analysis, Film Shot Classification, Cultural Heritage, Graph Neural Network.

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%.

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Paper citation in several formats:
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 - Volume 4: VISAPP, ISBN 978-989-758-555-5; ISSN 2184-4321, pages 991-998. DOI: 10.5220/0010905800003124

@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 - Volume 4: VISAPP,},
year={2022},
pages={991-998},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010905800003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Graph-based Shot Type Classification in Large Historical Film Archives
SN - 978-989-758-555-5
IS - 2184-4321
AU - Helm, D.
AU - Kleber, F.
AU - Kampel, M.
PY - 2022
SP - 991
EP - 998
DO - 10.5220/0010905800003124