Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/25270
Título: Shot classification and keyframe detection for vision based speakers diarization in parliamentary debates
Autores/as: Marín Reyes, Pedro Antonio 
Lorenzo-Navarro, Javier 
Castrillón-Santana, Modesto 
Sánchez Nielsen, Elena
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Visual diarization
Re-identification
CNN classification
Biometric traits
Fecha de publicación: 2016
Editor/a: Springer 
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: 17th Conference of the Spanish-Association-for-Artificial-Intelligence (CAEPIA 2016) 
17th Conference of the Spanish Association for Artificial Intelligence, (CAEPIA 2016) 
Resumen: Automatic labelling of speakers is an essential task for speakers diarization in parliamentary debates given the huge amount of video data to annotate. In this paper, we address the speaker diarization problem as a visual speaker re-identification issue with a special emphasis on the analysis of different shot types. We propose two approaches that makes use of convolutional neural networks (CNN) and biometric traits for keyframe extraction.
URI: http://hdl.handle.net/10553/25270
ISBN: 978-3-319-44635-6
ISSN: 0302-9743
DOI: 10.1007/978-3-319-44636-3_5
Fuente: Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science, v. 9868 LNCS, p. 48-57
Colección:Capítulo de libro
miniatura
preprint
Adobe PDF (9 MB)
Vista completa

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.