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Tracking Large-Scale Video Remix in Real-World Events

Date

Authors

Xie, Lexing

Natsev, Apostol

He, Xuming

Kender, John R

Hill, Matthew L.

Smith, John R

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

Content sharing networks, such as YouTube, contain traces of both explicit online interactions (such as likes, comments, or subscriptions), as well as latent interactions (such as quoting, or remixing, parts of a video). We propose visual memes, or freque

Keywords

Keywords: Detection algorithm; Image database; On-line interactions; Predictive models; Quantitative evaluation; Statistical modeling; Video interactions; YouTube; Multimedia systems; Signal processing; Social networking (online); Forecasting Image databases; social networks; YouTube

URI

http://hdl.handle.net/1885/74888

Collections

ANU Research Publications

Source

IEEE Transactions on Multimedia

Type

Journal article

DOI

10.1109/TMM.2013.2264929

Restricted until

2037-12-31

Downloads

File

Description

01_Xie_Tracking_Large-Scale_Video_2013.pdf (1.47 MB)

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