A framework for automatic semantic video annotation

Altadmri, Amjad and Ahmed, Amr (2014) A framework for automatic semantic video annotation. Multimedia Tools and Applications, 72 (2). pp. 1167-1191. ISSN 1380-7501

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A framework for automatic semantic video annotation
Journal Paper in "Multimedia Tools and Applications"
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Abstract

The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the ‘semantic gap’. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation.

Keywords:Video Annotation, Semantic Video Annotation, Automatic Semantic Video Annotation, semantic gap, Video Retrieval, video search engine, Video Information Retrieval, Commonsense Knowledgebase, Commonsense Knowledgebases, Commonsense Knowledge bases, Video matching, Video Similarity
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
G Mathematical and Computer Sciences > G450 Multi-media Computing Science
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:8818
Deposited On:05 Apr 2013 08:14

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