Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
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ACM SIGIR Forum
ACM Computing Surveys (CSUR)
ACM Computing Surveys (CSUR)
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VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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WWW '03 Proceedings of the 12th international conference on World Wide Web
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Pattern Recognition Letters
Index-driven similarity search in metric spaces (Survey Article)
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)
ACM Transactions on Information Systems (TOIS)
Detection of near-duplicate images for web search
Proceedings of the 6th ACM international conference on Image and video retrieval
WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
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ACM Computing Surveys (CSUR)
A metric cache for similarity search
Proceedings of the 2008 ACM workshop on Large-Scale distributed systems for information retrieval
On caching search engine query results
Computer Communications
On Index-Free Similarity Search in Metric Spaces
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Approximate distributed metric-space search
Proceedings of the 9th workshop on Large-scale and distributed informational retrieval
BASIL: effective near-duplicate image detection using gene sequence alignment
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Similarity caching in large-scale image retrieval
Information Processing and Management: an International Journal
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In order to become an effective complement to traditional Web-scale text-based image retrieval solutions, content-based image retrieval must address scalability and efficiency issues. In this paper we investigate the possibility of caching the answers to content-based image retrieval queries in metric space, with the aim of reducing the average cost of query processing, and boosting the overall system throughput. Our proposal exploits the similarity between the query object and the cache content, and allows the cache to return approximate answers with acceptable quality guarantee even if the query processed has never been encountered in the past. Moreover, since popular images that are likely to be used as query have several near-duplicate versions, we show that our caching algorithm is robust, and does not suffer of cache pollution problems due to near-duplicate query objects. We report on very promising results obtained with a collection of one million high-quality digital photos. We show that it is worth pursuing caching strategies also in similarity search systems, since the proposed caching techniques can have a significant impact on performance, like caching on text queries has been proven effective for traditional Web search engines.