SAIVT-ADMRG @ MediaEval 2014 social event detection
| Contribuinte(s) |
Larson, Martha Lonescu, Bogdan Anguera, Xavier Eskevich, Maria Schedl, Markus Soleymani, Mohammad Petkos, Georgios Sutcliffe, Richard Choi, Jaeyoung Jones, Gareth J.F. |
|---|---|
| Data(s) |
2014
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| Resumo |
This paper outlines the approach taken by the Speech, Audio, Image and Video Technologies laboratory, and the Applied Data Mining Research Group (SAIVT-ADMRG) in the 2014 MediaEval Social Event Detection (SED) task. We participated in the event based clustering subtask (subtask 1), and focused on investigating the incorporation of image features as another source of data to aid clustering. In particular, we developed a descriptor based around the use of super-pixel segmentation, that allows a low dimensional feature that incorporates both colour and texture information to be extracted and used within the popular bag-of-visual-words (BoVW) approach. |
| Formato |
application/pdf |
| Identificador | |
| Publicador |
CEUR Workshop Proceedings |
| Relação |
http://eprints.qut.edu.au/79106/3/__staffhome.qut.edu.au_staffgroupm%24_meaton_Desktop_mediaeval2014_submission_64.pdf http://ceur-ws.org/Vol-1263/mediaeval2014_submission_64.pdf Denman, Simon, Dean, David, Fookes, Clinton, & Sridharan, Sridha (2014) SAIVT-ADMRG @ MediaEval 2014 social event detection. In Larson, Martha, Lonescu, Bogdan, Anguera, Xavier, Eskevich, Maria, Schedl, Markus, Soleymani, Mohammad, et al. (Eds.) Working Notes Proceedings of the MediaEval 2014 Multimedia Benchmark Workshop, CEUR Workshop Proceedings, Barcelona, Spain, pp. 1-2. |
| Direitos |
Copyright 2014 The Authors |
| Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
| Tipo |
Conference Paper |
