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%.