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 effective for traditional Web search engines. Copyright 2008 ACM.
Falchi, F., Lucchese, C., Perego, R., Rabitti, F., & Orlando, S. (2008). A metric cache for similarity search. In International Conference on Information and Knowledge Management, Proceedings (pp. 43–50). https://doi.org/10.1145/1458469.1458473