Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/60191
Title: Video categorisation mimicking text mining
Authors: Ortega-Leon, Cristian
Marín Reyes, Pedro A. 
Lorenzo-Navarro, Javier 
Castrillon-Santana, Modesto 
Sánchez-Nielsen, Elena
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Video classification
Text classification
Text mining
Semantic video tagging
Issue Date: 2019
Publisher: Springer
Project: Identificación Automática de Oradores en Sesiones Parlamentarias Usando Características Audiovisuales. 
Journal: Lecture Notes in Computer Science 
Conference: 15th International Work-Conference on Artificial Neural Networks (IWANN) 
15th International Work-Conference on Artificial Neural Networks, IWANN 2019 
Abstract: 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
Source: Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science, v. 11507LNCS, p. 292-301
Appears in Collections:Capítulo de libro
Show full item record

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.