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The YLI Multimedia Event Detection corpus is a public-domain index of videoswith annotations and computed features, specialized for research in multimediaevent detection (MED), i.e., automatically identifying what's happening in avideo by analyzing the audio and visual content. The videos indexed in theYLI-MED corpus are a subset of the larger YLI feature corpus, which is beingdeveloped by the International Computer Science Institute and LawrenceLivermore National Laboratory based on the Yahoo Flickr Creative Commons 100Million (YFCC100M) dataset. The videos in YLI-MED are categorized as depictingone of ten target events, or no target event, and are annotated for additionalattributes like language spoken and whether the video has a musical score. Theannotations also include degree of annotator agreement and average annotatorconfidence scores for the event categorization of each video. Version 1.0 ofYLI-MED includes 1823 "positive" videos that depict the target events and48,138 "negative" videos, as well as 177 supplementary videos that are similarto event videos but are not positive examples. Our goal in producing YLI-MED isto be as open about our data and procedures as possible. This report describesthe procedures used to collect the corpus; gives detailed descriptivestatistics about the corpus makeup (and how video attributes affectedannotators' judgments); discusses possible biases in the corpus introduced byour procedural choices and compares it with the most similar existing dataset,TRECVID MED's HAVIC corpus; and gives an overview of our future plans forexpanding the annotation effort.

The YLI-MED Corpus: Characteristics, Procedures, and Plans