We present the Snake Table, an index structure designed for supporting streams of k-NN searches within a content-based similarity search framework. The index is created and updated in the online phase while resolving the queries, thus it does not need a preprocessing step. This index is intended to be used when the stream of query objects fits a snake distribution, that is, when the distance between two consecutive query objects is small. In particular, this kind of distribution is present in content-based video retrieval systems, when the set of query objects are consecutive frames from a query video. We show that the Snake Table improves the efficiency of k-NN searches in these systems, avoiding the building of a static index in the offline phase. © 2012 Springer-Verlag.
CITATION STYLE
Barrios, J. M., Bustos, B., & Skopal, T. (2012). Snake table: A dynamic pivot table for streams of k-NN searches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7404 LNCS, pp. 25–39). https://doi.org/10.1007/978-3-642-32153-5_3
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