Abstract
In this paper we present a fast approximate indexing method for high dimensional feature space that uses the error probability as an independent variable.
The idea of the algorithm is to define a low-dimensional feature space in which a significant portion of the inter-distance variance is concentrated, to search for the nearest neighborhood of the query in this space, and then to extend the search by a factor ζ to include a number of objects “near” this nearest neighborhood. We shall show that, under reasonable hypotheses on the distribution of items in the feature space, it is possible to derive a relation between the value ζ and the error probability.
We study the error probability and the complexity of the algorithm, validate the model using a data set of images, and show how the results can be used to design indexing schemes.
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Santini, S. (2013). Efficient Approximate Indexing in High-Dimensional Feature Spaces. In: Brisaboa, N., Pedreira, O., Zezula, P. (eds) Similarity Search and Applications. SISAP 2013. Lecture Notes in Computer Science, vol 8199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41062-8_20
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DOI: https://doi.org/10.1007/978-3-642-41062-8_20
Publisher Name: Springer, Berlin, Heidelberg
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