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2016 | OriginalPaper | Buchkapitel

Supermetric Search with the Four-Point Property

verfasst von : Richard Connor, Lucia Vadicamo, Franco Alberto Cardillo, Fausto Rabitti

Erschienen in: Similarity Search and Applications

Verlag: Springer International Publishing

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Abstract

Metric indexing research is concerned with the efficient evaluation of queries in metric spaces. In general, a large space of objects is arranged in such a way that, when a further object is presented as a query, those objects most similar to the query can be efficiently found. Most such mechanisms rely upon the triangle inequality property of the metric governing the space. The triangle inequality property is equivalent to a finite embedding property, which states that any three points of the space can be isometrically embedded in two-dimensional Euclidean space. In this paper, we examine a class of semimetric space which is finitely 4-embeddable in three-dimensional Euclidean space. In mathematics this property has been extensively studied and is generally known as the four-point property. All spaces with the four-point property are metric spaces, but they also have some stronger geometric guarantees. We coin the term supermetric space as, in terms of metric search, they are significantly more tractable. We show some stronger geometric guarantees deriving from the four-point property which can be used in indexing to great effect, and show results for two of the SISAP benchmark searches that are substantially better than any previously published.

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Fußnoten
1
All of the (Java) code for these experiments can be downloaded from https://​bitbucket.​org/​richardconnor/​metric-space-framework/​.
 
2
Which we therefore believe makes the best performance yet published for these metric/dataset combinations.
 
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Metadaten
Titel
Supermetric Search with the Four-Point Property
verfasst von
Richard Connor
Lucia Vadicamo
Franco Alberto Cardillo
Fausto Rabitti
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-46759-7_4

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