Efficient similarity search on vector sets

Abstract

Similarity search in database systems is becoming an increasingly important task in modern application domains such as multimedia, molecular biology, medical imaging, computer aided design and many others. Whereas most of the existing similarity models are based on feature vectors, there exist some models which use very complex object representations such as trees and graphs. A promising way between too simple and too complex object representations in similarity search are sets of feature vectors. In this paper, we first motivate the use of this modeling approach for complete object similarity search as well as for partial object similarity search. After introducing a distance measure between vector sets, suitable for many different ap- plication ranges, we present and discuss different filters which are indispensable for efficient query processing. In a broad experimental evaluation based on artificial and real-world test datasets, we show that our approach considerably outperforms both the sequential scan and metric index structures.