Abstract
Similarity search, finding objects similar to a given query object, is an important operation in multimedia databases, and has many applications in a wider variety of fields. As one approach to efficient similarity search, we focus on utilizing a set of pivots for reducing the number of similarity calculations between a query and each object in a database. In this paper, unlike conventional methods based on combinatorial optimization, we propose a new method for learning a set of pivots from existing data objects, in virtue of iterative numerical nonlinear optimization. In our experiments using one synthetic and two real data sets, we show that the proposed method significantly reduced the average number of similarity calculations, compared with some representative conventional methods.
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Kimura, M., Saito, K., Ueda, N. (2007). Pivot Learning for Efficient Similarity Search. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_28
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DOI: https://doi.org/10.1007/978-3-540-74829-8_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74828-1
Online ISBN: 978-3-540-74829-8
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