Graph-based Shot Type Classification in Large Historical Film Archives

Presentation by Daniel Helm at the "17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications"

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%.


This event poster shows written text with the event details only.
Monday, 07.02.2022, 16:45
Monday, 07.02.2022, 18:30
(GMT) Lisbon