Document - YFCC100M-HNfc6: a large-scale deep features benchmark for similarity search

2016

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YFCC100M-HNfc6: a large-scale deep features benchmark for similarity search

Amato G, Falchi F, Gennaro C, Rabitti F.

YFCC100M Deep features Content-based image retrieval Convolutional neural networks Similarity search

In this paper, we present YFCC100M-HNfc6, a benchmark consisting of 97M deep features extracted from the Yahoo Creative Commons 100M (YFCC100M) dataset. Three type of features were extracted using a state-of-the-art Convolutional Neural Network trained on the ImageNet and Places datasets. Together with the features, we made publicly available a set of 1,000 queries and k-NN results obtained by sequential scan. We first report detailed statistical information on both the features and search results. Then, we show an example of performance evaluation, performed using this benchmark, on the MI-File approximate similarity access method.

Source: SISAP 2016 - International Conference on Similarity Search and Applications, pp. 196–209, Tokyo, Japan, October 24-26, 2016

Publisher: Springer, Berlin, DEU

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@inproceedings{oai:it.cnr:prodotti:378990,
	title = {YFCC100M-HNfc6: a large-scale deep features benchmark for similarity search},
	author = {Amato G and Falchi F and Gennaro C and Rabitti F.},
	publisher = {Springer, Berlin, DEU},
	doi = {10.1007/978-3-319-46759-7_15},
	booktitle = {SISAP 2016 - International Conference on Similarity Search and Applications, pp. 196–209, Tokyo, Japan, October 24-26, 2016},
	year = {2016}
}