Temporal event clustering for digital photo collections
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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IEEE Transactions on Knowledge and Data Engineering
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Event detection from flickr data through wavelet-based spatial analysis
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Proceedings of the third ACM international conference on Web search and data mining
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Proceedings of the 1st ACM International Conference on Multimedia Retrieval
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Proceedings of the 1st ACM International Conference on Multimedia Retrieval
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Proceedings of the 20th ACM international conference on Information and knowledge management
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IEEE Transactions on Multimedia
Social event detection and retrieval in collaborative photo collections
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Social event detection using multimodal clustering and integrating supervisory signals
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Discovering inherent event taxonomies from social media collections
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Social event detection with interaction graph modeling
Proceedings of the 20th ACM international conference on Multimedia
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In this contribution, we propose a watershed-based method with support from external data sources and visual information to detect social events in web multimedia. The idea is based on two main observations: (1) people cannot be involved in more than one event at the same time, and (2) people tend to introduce similar annotations for all images associated to the same event. Based on these observations, the metadata is turned to an image so that each row contains all records belonging to one user; and these records are sorted by time. Thus, the social event detection is turned to watershed-based image segmentation, where Markers are generated by using (keyword, location, visual) features with support of external data sources, and the Flood progress is carried on by taking into account (tags set, time, visual) features. We test our algorithm on the MediaEval 2012 dataset both using only external data but also introducing visual information.