Document - Large scale indexing and searching deep convolutional neural network features

2016

Conference article Open Access

Large scale indexing and searching deep convolutional neural network features

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

Deep learning Image retrieval Convolutional neural network Inverted index

Content-based image retrieval using Deep Learning has become very popular during the last few years. In this work, we propose an approach to index Deep Convolutional Neural Network Features to support efficient retrieval on very large image databases. The idea is to provide a text encoding for these features enabling the use of a text retrieval engine to perform image similarity search. In this way, we built LuQ a robust retrieval system that combines full-text search with content-based image retrieval capabilities. In order to optimize the index occupation and the query response time, we evaluated various tuning parameters to generate the text encoding. To this end, we have developed a web-based prototype to efficiently search through a dataset of 100 million of images.

Source: DaWaK 2016 - 18th International Conference on Big Data Analytics and Knowledge Discovery, pp. 213–224, Porto, Portugal, 06-08/09/2016

Metrics

Back to previous page

Cite as

BibTeX entry

@inproceedings{oai:it.cnr:prodotti:378972,
	title = {Large scale indexing and searching deep convolutional neural network features},
	author = {Amato G and Debole F and Falchi F and Gennaro C and Rabitti F.},
	doi = {10.1007/978-3-319-43946-4_14},
	booktitle = {DaWaK 2016 - 18th International Conference on Big Data Analytics and Knowledge Discovery, pp. 213–224, Porto, Portugal, 06-08/09/2016},
	year = {2016}
}