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 files are available for download via HTTP. Link: https://purl.stanford.edu/zx935qw7203
The original link was: http://blackhole1.stanford.edu/vidsearch/dataset/stanfordi2v.html
References and Citation
Use of the datasets in published work should be acknowledged by a full citation to the authors' papers [ACC15] at the MMSys conference (Proceedings of ACM MMSys '15, Portland, Oregon, March 18-20, 2015).
ACC15: A. Araujo, J. Chaves, D. Chen, R. Angst and B. Girod. Stanford I2V: A News Video Dataset for Query-by-Image Experiments, Proceedings of ACM MMSys '15, Portland, Oregon, March 18-20, 2015.