Conference proceedings

YFCC100M HybridNet fc6 Deep Features for Content-Based Image Retrieval

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

Proceedings of the 2016 ACM Workshop on Multimedia COMMONS - MMCommons '16 (2016) pp. 11-18 Published by ACM Press

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Abstract

© 2016 Copyright held by the owner/author(s). 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.

Author-supplied keywords

  • YFCC100M
  • content-based image retrieval
  • deep features
  • multimedia information retrieval

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Authors

  • Giuseppe Amato

  • Fabrizio Falchi

  • Claudio Gennaro

  • Fausto Rabitti

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