Amato G., Carrara F., Falchi F., Gennaro C., Rabitti F., Vadicamo L.
Image retrieval Deep Features Inverted index
The great success of visual features learned from deep neu-ral networks has led to a significant effort to develop efficient and scal- A ble technologies for image retrieval. This paper presents an approach to transform neural network features into text codes suitable for being indexed by a standard full-text retrieval engine such as Elasticsearch. The basic idea is providing a transformation of neural network features with the twofold aim of promoting the sparsity without the need of un-supervised pre-training. We validate our approach on a recent convolu-tional neural network feature, namely Regional Maximum Activations of Convolutions (R-MAC), which is a state-of-art descriptor for image retrieval. An extensive experimental evaluation conducted on standard benchmarks shows the effectiveness and efficiency of the proposed ap-proach and how it compares to state-of-the-art main-memory indexes.
Source: 28th Italian Symposium on Advanced Database Systems, pp. 258–265, Virtual (online) due COVID-19, 21-24/06/2020
@inproceedings{oai:it.cnr:prodotti:437974, title = {Scalar Quantization-Based Text Encoding for Large Scale Image Retrieval}, author = {Amato G. and Carrara F. and Falchi F. and Gennaro C. and Rabitti F. and Vadicamo L.}, booktitle = {28th Italian Symposium on Advanced Database Systems, pp. 258–265, Virtual (online) due COVID-19, 21-24/06/2020}, year = {2020} }
Amato, Giuseppe0000-0003-0171-4315
Carrara, Fabio0000-0001-5014-5089
Falchi, Fabrizio0000-0001-6258-5313
Gennaro, Claudio0000-0002-3715-149X
Rabitti, Fausto0000-0002-2909-7745
Vadicamo, Lucia0000-0001-7182-7038
Networked Multimedia Information System (2002-2020)
Artificial Intelligence for Media and Humanities (2021-ongoing)