Abstract
The signature quadratic form distance and feature signatures have become a respected similarity space for effective content-based retrieval. Furthermore, the similarity space is configurable by a parameter alpha affecting both retrieval precision and intrinsic dimensionality, and thus interesting trade-offs can be achieved when a metric index is used for exact search. In this paper we combine such configurable model with state of the art approximate search techniques developed for the M-Index. In the experiments, we show that employing a configuration resulting in the best effectiveness of the measure leads also to very competitive approximate search effectiveness when using the M-Index, regardless the high intrinsic dimensionality of the corresponding similarity space.
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Lokoč, J., Grošup, T., Skopal, T. (2013). On Scalable Approximate Search with the Signature Quadratic Form Distance. 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_31
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DOI: https://doi.org/10.1007/978-3-642-41062-8_31
Publisher Name: Springer, Berlin, Heidelberg
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