Abstract
Similarity searching consists of retrieving elements from a database that are closest to a given query. One strategy selects some elements as references and uses them to organize the whole database. With these reference points, it is possible to obtain a candidate list that contains the answer to the query and the rest of the database can be discarded for a while. In this article, a new strategy for reducing the candidate list is proposed. According to the experiments presented, it is possible to reduce the size of the list by up to 35%.
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Figueroa, K., Camarena-Ibarrola, A., Reyes, N. (2021). Shortening the Candidate List for Similarity Searching Using Inverted Index. In: Roman-Rangel, E., Kuri-Morales, Á.F., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2021. Lecture Notes in Computer Science(), vol 12725. Springer, Cham. https://doi.org/10.1007/978-3-030-77004-4_9
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DOI: https://doi.org/10.1007/978-3-030-77004-4_9
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