Extendible hashing—a fast access method for dynamic files
ACM Transactions on Database Systems (TODS)
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
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
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Approximate similarity retrieval with M-trees
The VLDB Journal — The International Journal on Very Large Data Bases
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
Pivot selection techniques for proximity searching in metric spaces
Pattern Recognition Letters
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
M-Chord: a scalable distributed similarity search structure
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
Counting Distance Permutations
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
MESSIF: metric similarity search implementation framework
DELOS'07 Proceedings of the 1st international conference on Digital libraries: research and development
On locality-sensitive indexing in generic metric spaces
Proceedings of the Third International Conference on SImilarity Search and APplications
Audio similarity retrieval engine
Proceedings of the Third International Conference on SImilarity Search and APplications
Stabilizing the recall in similarity search
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Versatile probability-based indexing for approximate similarity search
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Approximate distributed metric-space search
Proceedings of the 9th workshop on Large-scale and distributed informational retrieval
Similarity caching in large-scale image retrieval
Information Processing and Management: an International Journal
Use of permutation prefixes for efficient and scalable approximate similarity search
Information Processing and Management: an International Journal
Modelling efficient novelty-based search result diversification in metric spaces
Journal of Discrete Algorithms
On Combining Sequence Alignment and Feature-Quantization for Sub-Image Searching
International Journal of Multimedia Data Engineering & Management
Efficiency and security in similarity cloud services
Proceedings of the VLDB Endowment
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Metric space as a universal and versatile model of similarity can be applied in various areas of non-text information retrieval. However, a general, efficient and scalable solution for metric data management is still a resisting research challenge. We introduce a novel indexing and searching mechanism called Metric Index (M-Index), that employs practically all known principles of metric space partitioning, pruning and filtering. The heart of the M-Index is a general mapping mechanism that enables to actually store the data in well-established structures such as the B+-tree or even in a distributed storage. We have implemented the M-Index with B+-tree and performed experiments on a combination of five MPEG-7 descriptors in a database of hundreds of thousands digital images. The experiments put under test several M-Index variants and compare them with two orthogonal approaches – the PM-Tree and the iDistance. The trials show that the M-Index outperforms the others in terms of efficiency of search-space pruning, I/O costs, and response times for precise similarity queries. Furthermore, the M-Index demonstrates an excellent ability to keep similar data close in the index which makes its approximation algorithm very efficient – maintaining practically constant response times while preserving a very high recall as the dataset grows.