GCap: Graph-based Automatic Image Captioning
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 9 - Volume 09
Learning similarity metrics for event identification in social media
Proceedings of the third ACM international conference on Web search and data mining
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Finding media illustrating events
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Heterogeneous features and model selection for event-based media classification
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
Jointly exploiting visual and non-visual information for event-related social media retrieval
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
A novel method for geographical social event detection in social media
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
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This paper focuses on detecting social, physical-world events from photos posted on social media sites. The problem is important: cheap media capture devices have significantly increased the number of photos shared on these sites. The main contribution of this paper is to incorporate online social interaction features in the detection of physical events. We believe that online social interaction reflect important signals among the participants on the "social affinity" of two photos, thereby helping event detection. We compute social affinity via a random-walk on a social interaction graph to determine similarity between two photos on the graph. We train a support vector machine classifier to combine the social affinity between photos and photo-centric metadata including time, location, tags and description. Incremental clustering is then used to group photos to event clusters. We have very good results on two large scale real-world datasets: Upcoming and MediaEval. We show an improvement between 0.06-0.10 in F1 on these datasets.