Event discovery from single pictures is a challenging problem that has raised significant interest in the last decade. During this time, a number of interesting solutions have been proposed to tackle event discovery in still images. However, a large scale benchmarking image dataset for the evaluation and comparison of event discovery algorithms from single images is still lagging behind. To this aim, in this paper we provide a large-scale properly annotated and balanced dataset of 490,000 images, covering every aspect of 14 different types of social events, selected among the most shared ones in the social network. In the dataset we tried our best to cover every aspect of the considered social events by collecting images for the same event-types with diverse contents in terms of viewpoints, colors, group pictures vs. single portrait and outdoor vs. indoor images, where the high variability of the represented information can be effectively explored to ensure better performances in event classification. Such a large-scale collection of event-related images is intended to become a powerful support tool for the research community in multimedia analysis by providing a common benchmark for training, testing, validation and comparison of existing and novel algorithms.
The files are available for download via HTTP. Link: http://loki.disi.unitn.it/~used/ Direct Link (Zip file containing labels in csv format): http://loki.disi.unitn.it/~used/CSV-files-for-SED-EiMM.zip Direct Link Datasets (Training Set): http://loki.disi.unitn.it/~used/USED-training.tar.gz Direct Link Datasets (Test Set): http://loki.disi.unitn.it/~used/USED-test.tar.gz
References and Citation
Use of the datasets in published work should be acknowledged by a full citation to the authors’ papers [ACB16] at the MMSys conference: Proceedings of ACM MMSys’16, Klagenfurt am Wörthersee, Austria, May 10-13, 2016.
ACB16: Ahmad, K., Conci, N., Boato, G., De Natale, F.G.B. USED: A large-scale social event detection dataset, Proceedings of the 7th International Conference on Multimedia Systems, MMSys 2016, pp. 380-385.