V3C1 Dataset: An Evaluation of Content Characteristics

Fabian Berns, Luca Rossetto, Klaus Schöffmann, Christian Beeks, George Awad
In Proceedings
Appears in
Proceedings of the International Conference on Multimedia Retrieval (ICMR)
Ottawa, ON, Canada

In this work we analyze content statistics of the V3C1 dataset, which is the first partition of the Vimeo Creative Commons Collection (V3C). The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, and will serve as evaluation basis for the Video Browser Showdown 2019-2021 and TREC Video Retrieval (TRECVID) Ad-Hoc Video Search tasks 2019-2021. The dataset comes with a shot segmentation (around 1 million shots) for which we analyze content specifics and statistics.
Our research shows that the content of V3C1 is very diverse, has no predominant characteristics and provides a low self-similarity. Thus it is very well suited for video retrieval evaluations as well as for participants of TRECVID AVS or the VBS.

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