  The YLI Multimedia Event Detection corpus is a public-domain index of videos
with annotations and computed features, specialized for research in multimedia
event detection (MED), i.e., automatically identifying what's happening in a
video by analyzing the audio and visual content. The videos indexed in the
YLI-MED corpus are a subset of the larger YLI feature corpus, which is being
developed by the International Computer Science Institute and Lawrence
Livermore National Laboratory based on the Yahoo Flickr Creative Commons 100
Million (YFCC100M) dataset. The videos in YLI-MED are categorized as depicting
one of ten target events, or no target event, and are annotated for additional
attributes like language spoken and whether the video has a musical score. The
annotations also include degree of annotator agreement and average annotator
confidence scores for the event categorization of each video. Version 1.0 of
YLI-MED includes 1823 "positive" videos that depict the target events and
48,138 "negative" videos, as well as 177 supplementary videos that are similar
to event videos but are not positive examples. Our goal in producing YLI-MED is
to be as open about our data and procedures as possible. This report describes
the procedures used to collect the corpus; gives detailed descriptive
statistics about the corpus makeup (and how video attributes affected
annotators' judgments); discusses possible biases in the corpus introduced by
our procedural choices and compares it with the most similar existing dataset,
TRECVID MED's HAVIC corpus; and gives an overview of our future plans for
expanding the annotation effort.
