Video summarisation by deep visual and categorical diversity

Autor: Pedro Atencio, Sánchez‐Torres German, John William Branch, Claudio Delrieux
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: IET Computer Vision, Vol 13, Iss 6, Pp 569-577 (2019)
Druh dokumentu: article
ISSN: 1751-9640
1751-9632
DOI: 10.1049/iet-cvi.2018.5436
Popis: The authors propose a video‐summarisation method based on visual and categorical diversities using pre‐trained deep visual and categorical models. Their method extracts visual and categorical features from a pre‐trained deep convolutional network (DCN) and a pre‐trained word‐embedding matrix. Using visual and categorical information they obtain a video diversity estimation, which is used as an importance score to select segments from the input video that best describes it. Their method also allows performing queries during the search process, in this way personalising the resulting video summaries according to the particular intended purposes. The performance of the method is evaluated using different pre‐trained DCN models in order to select the architecture with the best throughput. They then compare it with other state‐of‐the‐art proposals in video summarisation using a data‐driven approach with the public dataset SumMe, which contains annotated videos with per‐fragment importance. The results show that their method outperforms other proposals in most of the examples. As an additional advantage, their method requires a simple and direct implementation that does not require a training stage.
Databáze: Directory of Open Access Journals
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