Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2011

Abstract

Despite the massive growth of web-shared videos in Internet, efficient organization and monitoring of videos remains a practical challenge. While nowadays broadcasting channels are keen to monitor online events, identifying topics of interest from huge volume of user uploaded videos and giving recommendation to emerging topics are by no means easy. Specifically, such process involves discovering of new topic, visualization of the topic content, and incremental monitoring of topic evolution. This paper studies the problem from three aspects. First, given a large set of videos collected over months, an efficient algorithm based on salient trajectory extraction on a topic evolution link graph is proposed for topic discovery. Second, topic trajectory is visualized as a temporal graph in 2-D space, with one dimension as time and another as degree of hotness, for depicting the birth, growth, and decay of a topic. Finally, giving the previously discovered topics, an incremental monitoring algorithm is proposed to track newly uploaded videos, while discovering new topics and giving recommendation to potentially hot topics. We demonstrate the application on three months' videos crawled from YouTube during December 2008 to February 2009. Both objective and user studies are conducted to verify the performance.

Keywords

Topic trajectory mining, video recommendation, visualization

Discipline

Data Storage Systems | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Circuits and Systems for Video Technology

Volume

21

Issue

12

First Page

1835

Last Page

1846

ISSN

1051-8215

Identifier

10.1109/TCSVT.2011.2148470

Publisher

Institute of Electrical and Electronics Engineers

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