Detection of Social Events in Streams of Social Multimedia
- Submitting institution
-
University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 20753395
- Type
- D - Journal article
- DOI
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10.1007/s13735-015-0085-0
- Title of journal
- International Journal of Multimedia Information Retrieval
- Article number
- -
- First page
- 289
- Volume
- 4
- Issue
- 4
- ISSN
- 2192-6611
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- With ever increasing amounts of data being generated, streaming algorithms, which avoid the need to consider all data at once are important to tractably mine information. This work buildings upon strong theoretical foundations which gave the best performing in the MediaEval 2013 Social Event Detection task (https://bit.ly/39p2foH), involving international teams evaluating approaches on a large dataset (https://bit.ly/2P1FHTp). The paper thoroughly grounds, expands upon and evaluates the ideas from MediaEval. This led to international recognition and was the basis of a keynote at the ACM ICMR 2014 Workshop on Social Events in Web Multimedia (https://bit.ly/2UEL3rc).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -