Abstract
In this article, we present an approximate technique that allows accelerating similarity search in high dimensional vector spaces. The presented approach, called HiPeR, is based on a hierarchy of subspaces and indices: it performs nearest neighbors search across spaces of different dimensions, starting with the lowest dimensions up to the highest ones, aiming at minimizing the effects of the curse of dimensionality. In this work, HiPeR has been implemented on the classical index structure VA-File, providing VA-Hierarchies. The model of precision loss defined is probabilistic and non parametric and quality of answers can be selected by user at query time. HiPeR is evaluated for range queries on 3 real data-sets of image descriptors varying from 500,000 vectors to 4 millions. The experiments show that this approximate technique improves retrieval by saving I/O access significantly.
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Bouteldja, N., Gouet-Brunet, V., Scholl, M. (2009). Approximate Retrieval with HiPeR: Application to VA-Hierarchies. In: Huet, B., Smeaton, A., Mayer-Patel, K., Avrithis, Y. (eds) Advances in Multimedia Modeling . MMM 2009. Lecture Notes in Computer Science, vol 5371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92892-8_36
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DOI: https://doi.org/10.1007/978-3-540-92892-8_36
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