Speeding up Similarity Search by Sketches

  • Vladimir MicEmail author
  • David Novak
  • Pavel Zezula
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Masaryk UniversityBrnoCzech Republic

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