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
Approximate similarity retrieval with M-trees
The VLDB Journal — The International Journal on Very Large Data Bases
Locality-sensitive hashing scheme based on p-stable distributions
SCG '04 Proceedings of the twentieth annual symposium on Computational geometry
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Multi-probe LSH: efficient indexing for high-dimensional similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Effective Proximity Retrieval by Ordering Permutations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling LSH for performance tuning
Proceedings of the 17th ACM conference on Information and knowledge management
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
CoPhIR Image Collection under the Microscope
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Metric Index: An Efficient and Scalable Solution for Similarity Search
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Building a web-scale image similarity search system
Multimedia Tools and Applications
On locality-sensitive indexing in generic metric spaces
Proceedings of the Third International Conference on SImilarity Search and APplications
Proximity searching in high dimensional spaces with a proximity preserving order
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Polyphasic metric index: reaching the practical limits of proximity searching
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
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The recent techniques for approximate similarity search focus on optimizing answer precision/recall and they typically improve the average of these measures over a set of sample queries. However, according to our observation, the recall for particular indexes and queries can fluctuate considerably. In order to stabilize the recall, we propose a query-evaluation model that exploits several variants of the search index. This approach is applicable to a significant subset of current approximate methods with a focus on techniques based purely on metric postulates. Applying this approach to the M-Index structure, we perform extensive measurements on large datasets and we show that this approach has a positive impact on the recall stability and it suppresses the most unsatisfactory cases. Further, the results indicate that the proposed approach can also increase the general average recall for given overall search costs.