Document - YFCC100M HybridNet fc6 deep features for content-based image retrieval

2016

Conference article Open Access

YFCC100M HybridNet fc6 deep features for content-based image retrieval

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

YFCC100M Deep Features Multimedia Information Retrieval Content-Based Image Retrieval

This paper presents a corpus of deep features extracted from the YFCC100M images considering the fc6 hidden layer activation of the HybridNet deep convolutional neural network. For a set of random selected queries we made available k-NN results obtained sequentially scanning the entire set features comparing both using the Euclidean and Hamming Distance on a binarized version of the features. This set of results is ground truth for evaluating Content-Based Image Retrieval (CBIR) systems that use approximate similarity search methods for efficient and scalable indexing. Moreover, we present experimental results obtained indexing this corpus with two distinct approaches: the Metric Inverted File and the Lucene Quantization. These two CBIR systems are public available online allowing real-time search using both internal and external queries.

Source: MMCommons 2016 - ACM Workshop on the Multimedia COMMONS, pp. 11–18, Amsterdam, The Netherlands, 16 October 2016

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@inproceedings{oai:it.cnr:prodotti:378974,
	title = {YFCC100M HybridNet fc6 deep features for content-based image retrieval},
	author = {Amato G and Falchi F and Gennaro C and Rabitti F.},
	doi = {10.1145/2983554.2983557},
	booktitle = {MMCommons 2016 - ACM Workshop on the Multimedia COMMONS, pp. 11–18, Amsterdam, The Netherlands, 16 October 2016},
	year = {2016}
}