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

Contributo in Atti di convegno
Data di Pubblicazione:
2016
Abstract:
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.
Tipologia CRIS:
04.01 Contributo in Atti di convegno
Keywords:
Similarity search; Deep features; Content-based image retrieval; Convolutional neural networks; YFCC100M
Elenco autori:
Falchi, Fabrizio; Amato, Giuseppe; Gennaro, Claudio; Rabitti, Fausto
Autori di Ateneo:
AMATO GIUSEPPE
FALCHI FABRIZIO
GENNARO CLAUDIO
Link alla scheda completa:
https://iris.cnr.it/handle/20.500.14243/339648
Titolo del libro:
Similarity Search and Applications
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URL

https://link.springer.com/chapter/10.1007/978-3-319-46759-7_15
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