Tracking web video topics: Discovery, visualization, and monitoring

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 recom...

Full description

Saved in:
Bibliographic Details
Main Authors: CAO, Juan, NGO, Chong-wah, ZHANG, Yong-Dong, LI, Jin-Tao
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2011
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/6348
https://ink.library.smu.edu.sg/context/sis_research/article/7351/viewcontent/tcsvt11_caojuan.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
Description
Summary: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.