2022
Conference article  Open Access

Approximate nearest neighbor search on standard search engines

Carrara F., Vadicamo L., Gennaro C., Amato G.

Surrogate text representation  Inverted index  Approximate search  High-dimensional indexing  Very large databases 

Approximate search for high-dimensional vectors is commonly addressed using dedicated techniques often combined with hardware acceleration provided by GPUs, FPGAs, and other custom in-memory silicon. Despite their effectiveness, harmonizing those optimized solutions with other types of searches often poses technological difficulties. For example, to implement a combined text+image multimodal search, we are forced first to query the index of high-dimensional image descriptors and then filter the results based on the textual query or vice versa. This paper proposes a text surrogate technique to translate real-valued vectors into text and index them with a standard textual search engine such as Elasticsearch or Apache Lucene. This technique allows us to perform approximate kNN searches of high-dimensional vectors alongside classical full-text searches natively on a single textual search engine, enabling multimedia queries without sacrificing scalability. Our proposal exploits a combination of vector quantization and scalar quantization. We compared our approach to the existing literature in this field of research, demonstrating a significant improvement in performance through preliminary experimentation.

Source: SISAP 2022 - 15th International Conference on Similarity Search and Applications, pp. 214–221, Bologna, Italy, 7-9/10/2022


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BibTeX entry
@inproceedings{oai:it.cnr:prodotti:471829,
	title = {Approximate nearest neighbor search on standard search engines},
	author = {Carrara F. and Vadicamo L. and Gennaro C. and Amato G.},
	doi = {10.1007/978-3-031-17849-8_17},
	booktitle = {SISAP 2022 - 15th International Conference on Similarity Search and Applications, pp. 214–221, Bologna, Italy, 7-9/10/2022},
	year = {2022}
}

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