Automatic video annotation framework using concept detectors

Citation

Chamasemani, Fereshteh Falah and Affendey, Lilly Suriani and Mustapha, Norwati and Khalid, Fatimah (2015) Automatic video annotation framework using concept detectors. Journal of Applied Sciences, 15 (2). pp. 256-263. ISSN 1812-5654; ESSN: 1812-5662

Abstract

Automatic video annotation has received a great deal of attention from researchers working on video retrieval. This study presents a novel automatic video annotation framework to enhance the annotation accuracy and reduce the processing time in large-scale video data by utilizing semantic concepts. The proposed framework consists of three main modules i.e., pre-processing, video analysis and annotation module. The framework support an efficient search and retrieval for any video content analysis and video archive applications. The experimental results on widely used TRECVID dataset using concepts of Columbia374 demonstrate the effectiveness of the proposed framework in assigning appropriate and semantically representative annotations for any new video.

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Additional Metadata

Item Type:	Article
Divisions:	Faculty of Computer Science and Information Technology
DOI Number:	https://doi.org/10.3923/jas.2015.256.263
Publisher:	Asian Network for Scientific Information
Keywords:	Content-based video retrieval; Video annotation; Semantic vido annotation; Image retrieval; Videoa concept detection
Depositing User:	Mohd Hafiz Che Mahasan
Date Deposited:	27 Oct 2016 09:00
Last Modified:	27 Oct 2016 09:00
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3923/jas.2015.256.263
URI:	http://psasir.upm.edu.my/id/eprint/43900
Statistic Details:	View Download Statistic

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