Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2013

Abstract

Concept detection is probably the most important research problem in the area of multimedia. The need to model with sufficient and diverse training instances, however, makes the task computationally and resourcefully expensive. Meanwhile, the popularity of social media has generated massive amount of weakly tagged images which could be leveraged for concept model learning. Therefore, in this paper, we consider exploring weakly taggedWeb images to shed some light on video concept detection. Particularly, two sets of Web images downloaded from Flickr are utilized as training data for concept detection on two real-world large-scale video datasets released by TRECVID. Our experiments are conducted under different settings with and without transfer learning. The results indicate that Web images are helpful in the case of few available training instances in video domain, which is a common case of many real-world applications.

Keywords

domain transfer, Video concept detection, Web image

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013, San Jose, CA, July 15-19

First Page

1

Last Page

6

ISBN

9781479916047

Identifier

10.1109/ICMEW.2013.6618377

Publisher

IEEE

City or Country

San Jose, CA

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