Pivot learning for efficient similarity search

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 non-linear 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. © Springer-Verlag Berlin Heidelberg 2007.