Dynamic Similarity Search in MultiMetric Spaces

Abstract

An important research issue in multimedia databases is the retrieval of similar objects. For most applications in multimedia databases, an exact search is not meaningful. Thus, much effort has been devoted to develop efficient and effective similarity search techniques. A recent approach, that has been shown to improve the effectiveness of similarity search in multimedia databases, resorts to the usage of combinations of metrics where the desirable contribution (weight) of each metric is chosen at query time. This paper presents the Multi-Metric M-tree (M3-tree), a metric access method that supports similarity queries with dynamic combinations of metric functions. The M3-tree, an extension of the Mtree, stores partial distances to better estimate the weighed distances between routing/ground entries and each query, where a single distance function is used to build the whole index. An experimental evaluation shows that the M3-tree may be as efficient as having multiple M-trees (one for each combination of metrics).