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Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases

Altadmri, Amjad and Ahmed, Amr (2009) Automatic semantic video annotation in wide domain videos based on similarity and commonsense knowledgebases. In: 2009 IEEE International Conference on Signal and Image Processing Applications. IEEE, pp. 74-79. ISBN 978-1-4244-5560-7. E-ISBN 978-1-4244-5561-4. (doi:10.1109/ICSIPA.2009.5478723)

Abstract

In this paper, we introduce a novel framework for automatic Semantic Video Annotation. As this framework detects possible events occurring in video clips, it forms the annotating base of video search engine. To achieve this purpose, the system has to able to operate on uncontrolled wide-domain videos. Thus, all layers have to be based on generic features.

This framework aims to bridge the "semantic gap", which is the difference between the low-level visual features and the human's perception, by finding videos with similar visual events, then analyzing their free text annotation to find a common area then to decide the best description for this new video using commonsense knowledgebases.

Experiments were performed on wide-domain video clips from the TRECVID 2005 BBC rush standard database. Results from these experiments show promising integrity between those two layers in order to find expressing annotations for the input video. These results were evaluated based on retrieval performance.

Item Type: Book section
DOI/Identification number: 10.1109/ICSIPA.2009.5478723
Uncontrolled keywords: Semantic Video Annotation, Video Indexing, Video Retrieval, video search engine, semantic gap, uncontrolled videos, Content based, Commonsense Knowledgebases, visual events, free text annotation, Event Detection, Wide Domain Videos, Similarity, Automatic Semantic Video Annotation, Video Annotation, Video Information Retrieval, Content based video retrieval, uncontrolled wide-domain videos, generic features, low-level visual features, human's perception, wide-domain video clips, TRECVID, TRECVID BBC rush, standard video database, retrieval performance.
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.575 Multimedia systems
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Amjad Altadmri
Date Deposited: 15 Jan 2015 22:06 UTC
Last Modified: 19 Sep 2019 15:22 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/45943 (The current URI for this page, for reference purposes)
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