ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Pivot selection techniques for proximity searching in metric spaces
Pattern Recognition Letters
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Effective Proximity Retrieval by Ordering Permutations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximate similarity search in metric spaces using inverted files
Proceedings of the 3rd international conference on Scalable information systems
Approximate similarity search: A multi-faceted problem
Journal of Discrete Algorithms
A Brief Index for Proximity Searching
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Succinct nearest neighbor search
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Efficient group of permutants for proximity searching
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Compact and efficient permutations for proximity searching
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
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Modeling proximity search problems as a metric space provides a general framework usable in many areas, like pattern recognition, web search, clustering, data mining, knowledge management, textual and multimedia information retrieval, to name a few. Metric indexes have been improved over the years and many instances of the problem can be solved efficiently. However, when very large/high dimensional metric databases are indexed exact approaches are not yet capable of solving efficiently the problem, the performance in these circumstances is degraded to almost sequential search. To overcome the above limitation, non-exact proximity searching algorithms can be used to give answers that either in probability or in an approximation factor are close to the exact result. Approximation is acceptable in many contexts, specially when human judgement about closeness is involved. In vector spaces, on the other hand, there is a very successful approach dubbed Locality Sensitive Hashing which consist in making a succinct representation of the objects. This succinct representation is relatively insensitive to small variations of the locality. Unfortunately, the hashing function have to be carefully designed, very close to the data model, and different functions are used when objects come from different domains. In this paper we give a new schema to encode objects in a general metric space with a uniform framework, independent from the data model. Finally, we provide experimental support to our claims using several real life databases with different data models and distance functions obtaining excellent results in both the speed and the recall sense, specially for large databases.