Optimizing query performance with inverted cache in metric spaces

Abstract

Similarity searching has become widely available in many on-line archives of multimedia content. Querying such systems starts with either a query object provided by user or a random object provided by the system, and proceeds in more iterations to improve user’s satisfaction with query results. This leads to processing many very similar queries by the system. In this paper, we analyze performance of two representatives of metric indexing structures and propose a novel concept of reordering search queue that optimizes access to data partitions for repetitive queries. This concept is verified in numerous experiments on real-life image dataset.