Using the Triangle Inequality to Reduce the Number of Comparisons Required for Similarity-Based Retrieval

Dissimilarity measures, the basis of similarity-based retrieval, can be viewed as a distance and a similarity-based search as a nearest neighbor search. Though there has been extensive research on data structures and search methods to support nearest-neighbor searching, these indexing and dimension-reduction methods are generally not applicable to non-coordinate data and non-Euclidean distance measures. In this paper we reexamine and extend previous work of other researchers on best match searching based on the triangle inequality. These methods can be used to organize both non-coordinate data and non-Euclidean metric similarity measures. The eeectiveness of the indexes depends on the actual dimen-sionality of the feature set, data, and similarity metric used. We show that these methods provide signiicant performance improvements and may be of practical value in real-world databases.