Zeitschriftenartikel:
M. Zaharieva, M. Del Fabro, M. Zeppelzauer:
"Cross-Platform Social Event Detection";
IEEE Multimedia,
22
(2015),
13;
S. 14
- 25.
Kurzfassung deutsch:
A large part of media shared on online platforms such as Flickr and YouTube is captured at various social events (such as music festivals, exhibitions, and sport events). While it is quite easy to share personal impressions online, it is much more challenging to identify content that is related to the same social event across different platforms. Here, the authors focus on the detection of social events in a data collection from Flickr and YouTube. They propose an unsupervised, multistaged approach that explores commonly available, real-world metadata for the detection and linking of social events across sharing platforms. The proposed methodology and the performed experiments allow for a thorough evaluation of the usefulness of available metadata in the context of social event detection in both single- and cross-platform scenarios. This article is part of a special issue on social multimedia and storytelling.
Kurzfassung englisch:
A large part of media shared on online platforms such as Flickr and YouTube is captured at various social events (such as music festivals, exhibitions, and sport events). While it is quite easy to share personal impressions online, it is much more challenging to identify content that is related to the same social event across different platforms. Here, the authors focus on the detection of social events in a data collection from Flickr and YouTube. They propose an unsupervised, multistaged approach that explores commonly available, real-world metadata for the detection and linking of social events across sharing platforms. The proposed methodology and the performed experiments allow for a thorough evaluation of the usefulness of available metadata in the context of social event detection in both single- and cross-platform scenarios. This article is part of a special issue on social multimedia and storytelling.
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/MMUL.2015.31
Zugeordnete Projekte:
Projektleitung Maia Zaharieva:
Unusual sequences detection in very large video collections