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Speeding up Similarity Search by Sketches

Part of the Lecture Notes in Computer Science book series (LNISA,volume 9939)


Efficient object retrieval based on a generic similarity is one of the fundamental tasks in the area of information retrieval. We propose an enhancement for techniques that use the distance-based model of similarity. This enhancement is based on sketches–compact bit strings compared by the Hamming distance which represent data objects from the original space. The sketches form an additional filter that reduce the number of accessed data objects while practically preserving the search quality. For a certain class of state-of-the-art techniques, we can create the sketches using already known information, thus the time overhead is negligible and the memory overhead is subtle. According to the presented experiments, the sketch filtering can reduce the number of accessed data objects by 60–80 % in case of M-Index, and 30 % in case of PPP-Codes index while hurting the recall by less than 0.4 % on 10-NN search.


  • Data Object
  • Convolutional Neural Network
  • Candidate Object
  • Indexing Technique
  • Query Object

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This work was supported by the Czech Science Foundation project GA16-18889S.

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Correspondence to Vladimir Mic .

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Mic, V., Novak, D., Zezula, P. (2016). Speeding up Similarity Search by Sketches. In: Amsaleg, L., Houle, M., Schubert, E. (eds) Similarity Search and Applications. SISAP 2016. Lecture Notes in Computer Science(), vol 9939. Springer, Cham.

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