Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/60191
Título: | Video categorisation mimicking text mining | Autores/as: | Ortega-Leon, Cristian Marín Reyes, Pedro A. Lorenzo-Navarro, Javier Castrillon-Santana, Modesto Sánchez-Nielsen, Elena |
Clasificación UNESCO: | 120304 Inteligencia artificial | Palabras clave: | Video classification Text classification Text mining Semantic video tagging |
Fecha de publicación: | 2019 | Editor/a: | Springer | Proyectos: | Identificación Automática de Oradores en Sesiones Parlamentarias Usando Características Audiovisuales. | Publicación seriada: | Lecture Notes in Computer Science | Conferencia: | 15th International Work-Conference on Artificial Neural Networks (IWANN) 15th International Work-Conference on Artificial Neural Networks, IWANN 2019 |
Resumen: | With the rapid growth of online videos on the Web, there is an increasing research interest in automatic categorisation of videos. It is essential for multimedia tasks in order to facilitate indexing, search and retrieval of available video files on the Web. In this paper, we propose a different technique for the video categorisation problem using only visual information. Entity labels extracted from each frame using a deep learning network, mimic words giving rise to manage the video classification task as a text mining problem. Experimental evaluation on two widely used datasets confirms that the proposing approach fits perfectly to video classification problems. Our approach achieves 64.30% in terms of Mean Average Precision (mAP) in CCV dataset, above other approaches that make use of both visual and audio information. | URI: | http://hdl.handle.net/10553/60191 | ISBN: | 978-3-030-20517-1 | ISSN: | 0302-9743 | DOI: | 10.1007/978-3-030-20518-8_25 | Fuente: | Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science, v. 11507LNCS, p. 292-301 |
Colección: | Capítulo de libro |
Citas SCOPUSTM
1
actualizado el 08-sep-2024
Visitas
113
actualizado el 02-mar-2024
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.