PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Lucchese C., Perego R., Rabitti F., Falchi F., Orlando S. A metric cache for similarity search. In: LSDS-IR '08 - Sixth Workshop on Large-Scale Distributed Systems for Information Retrieval (Napa Valley California, October 30 2008). Proceedings, pp. 43 - 50. ACM, 2008.
 
 
Abstract
(English)
Similarity search in metric spaces is a general paradigm that can be used in several application elds. It can also be ef- fectively exploited in content-based image retrieval systems, which are shifting their target towards theWeb-scale dimen- sion. In this context, an important issue becomes the design of scalable solutions, which combine parallel and distributed architectures with caching at several levels. To this end, we investigate the design of a similarity cache that works in metric spaces. It is able to answer with exact and approximate results: even when an exact match is not present in cache, our cache may return an approximate re- sult set with quality guarantees. By conducting tests on a collection of one million high-quality digital photos, we show that the proposed caching techniques can have a signi cant impact on performance, like caching on text queries has been proved e ective for traditional Web search engines.
DOI: 10.1145/1458469.1458473
Subject Cache
Content Based Image Retrieval
H.2.4 multimedia databases


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