Stanford I2V
André de Melo Araújo, Jason Chaves, David Chen, Roland Angst, Bernd Girod · 2015
Reproducible research in the area of visual search depends on the availability of large annotated datasets. In this paper, we address the problem of querying a video database by images that might share some contents with one or more video clips. We present a new large dataset, called Stanford I2V. We have collected more than 3; 800 hours of newscast videos and annotated more than 200 ground-truth queries. In the following, the dataset is described in detail, the collection methodology is outlined and retrieval performance for a benchmark algorithm is presented. These results may serve as a baseline for future research and provide an example of the intended use of the Stanford I2V dataset. The dataset can be downloaded at http://purl.stanford.edu/zx935qw7203.