movie2trailer:  Unsupervised trailer generation using  Anomaly detection

Rehusevych, Orest

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dc.contributor.author	Rehusevych, Orest
dc.date.accessioned	2024-02-14T15:39:11Z
dc.date.available	2024-02-14T15:39:11Z
dc.date.issued	2019
dc.identifier.citation	Rehusevych, Orest. movie2trailer: Unsupervised trailer generation using Anomaly detection / Rehusevych, Orest; Supervisor: Taras Firman; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2019. – 45 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4472
dc.language.iso	en	uk
dc.title	movie2trailer: Unsupervised trailer generation using Anomaly detection	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	In this work, we present a novel unsupervised approach for automatic trailer gen- eration - movie2trailer. To our knowledge, it is the first-ever application of anomaly detection to such a creative and challenging part of the trailer creation process as a shot selection. One of the main advantages of our approach over the competitors is that it does not require any prior knowledge and extracts all needed information directly from the input movie. By leveraging the recent advancements in video and audio analysis, we can produce high-quality movie trailers in equal or less time than professional movie editors. The proposed approach reaches state-of-the-art in terms of visual attractiveness and closeness to the "real" trailer. Moreover, it exposes new horizons for researching anomaly detection applications in the movie industry. An example of generated with our approach trailer for the movie "Requiem for a dream" can be observed on YouTube.	uk