Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
ACM Computing Surveys (CSUR)
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
M-Grid: similarity searching in grid
P2PIR '06 Proceedings of the international workshop on Information retrieval in peer-to-peer networks
Similarity join in metric spaces
ECIR'03 Proceedings of the 25th European conference on IR research
IEEE Transactions on Information Theory
On the configuration of the similarity search data structure d-index for high dimensional objects
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Similarity grid for searching in metric spaces
DELOS'04 Proceedings of the 6th Thematic conference on Peer-to-Peer, Grid, and Service-Orientation in Digital Library Architectures
Self-organising hierarchical retrieval in a case-agent system
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
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A novel access structure for similarity search in metric databases, called Similarity Hashing (SH), is proposed. It is a multi-level hash structure, consisting of search-separable bucket sets on each level. The structure supports easy insertion and bounded search costs, because at most one bucket needs to be accessed at each level for range queries up to a pre-defined value of search radius. At the same time, the pivot-based strategy significantly reduces the number of distance computations. Contrary to tree organizations, the SH structure is suitable for distributed and parallel implementations.