2020
Conference article  Open Access

Scalar Quantization-Based Text Encoding for Large Scale Image Retrieval

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



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@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}
}