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A Survey on Video Annotations Different Techniques

Archana V. Potnurwar, Mohammad Atique Published in Data Mining

IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013
Year of Publication: 2013
© 2012 by IJAIS Journal
10.5120/icwac1334
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  1. Archana V Potnurwar and Mohammad Atique. Article: A Survey on Video Annotations Different Techniques. IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013 ICWAC(3):16-19, June 2013. BibTeX

    @article{key:article,
    	author = "Archana V. Potnurwar and Mohammad Atique",
    	title = "Article: A Survey on Video Annotations Different Techniques",
    	journal = "IJAIS Proceedings on International Conference and workshop on Advanced Computing 2013",
    	year = 2013,
    	volume = "ICWAC",
    	number = 3,
    	pages = "16-19",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

The insufficiency of labeled training data for representing the distribution of the entire dataset is a major obstacle in automatic semantic annotation of large-scale video database. Semi-supervised learning algorithms, which attempt to learn from both labeled and unlabeled data, are promising to solve this problem. In this paper ,retrieving videos using key words requires obtaining the semantic features of the videos. Most work reported in the literature focuses on annotating a video shot with a fixed number of key words, no matter how much information is contained in the video shot.

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Keywords

Videoannotations, multimodal, automatic video annotations, domain specific