Artículo
Video summarisation by deep visual and categorical diversity
Fecha de publicación:
13/05/2019
Editorial:
Institution of Engineering and Technology
Revista:
Iet Computer Vision
ISSN:
1751-9632
e-ISSN:
1751-9640
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
Palabras clave:
VIDEO SUMMARIZATION METHOD
,
TRANSFER LEARNING
,
DCN
Archivos asociados
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Identificadores
Colecciones
Articulos(CCT - BAHIA BLANCA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - BAHIA BLANCA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - BAHIA BLANCA
Citación
Atencio, Pedro; Sanchez Torres, German; Branch, John; Delrieux, Claudio Augusto; Video summarisation by deep visual and categorical diversity; Institution of Engineering and Technology; Iet Computer Vision; 13; 6; 13-5-2019; 569-577
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