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Speeding up Similarity Search by Sketches

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Book cover Similarity Search and Applications (SISAP 2016)

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Abstract

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.

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Notes

  1. 1.

    http://disa.fi.muni.cz/profiset/.

  2. 2.

    http://cophir.isti.cnr.it/.

References

  1. Amato, G., Gennaro, C., Savino, P.: MI-File: using inverted files for scalable approximate similarity search. Multimedia Tools Appl. 71(3), 1333–1362 (2014)

    Article  Google Scholar 

  2. Batko, M., Falchi, F., Lucchese, C., Novak, D., Perego, R., Rabitti, F., Sedmidubsky, J., Zezula, P.: Building a web-scale image similarity search system. Multimedia Tools Appl. 47(3), 599–629 (2010)

    Article  Google Scholar 

  3. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: DeCAF: a deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:1310.1531 (2013)

  4. Dong, W., Charikar, M., Li, K.: Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces. In: Proceedings of ACM SIGIR 2008, pp. 123–130. ACM (2008)

    Google Scholar 

  5. Esuli, A.: Use of permutation prefixes for efficient and scalable approximate similarity search. Inf. Process. Manage. 48(5), 889–902 (2012)

    Article  Google Scholar 

  6. Kemler, D.G.: Classification in young and retarded children: the primacy of overall similarity relations. Child Dev. 53(3), 768–779 (1982)

    Article  Google Scholar 

  7. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)

    Google Scholar 

  8. Mic, V., Novak, D., Zezula, P.: Improving sketches for similarity search. In: Proceedings of MEMICS 2015, pp. 45–57 (2015)

    Google Scholar 

  9. MPEG7: Multimedia content description interfaces. part 3: Visual (2002)

    Google Scholar 

  10. Muja, M., Lowe, D.G.: Scalable nearest neighbour algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 1–14 (2014)

    Article  Google Scholar 

  11. Muller-Molina, A.J., Shinohara, T.: Efficient similarity search by reducing i/o with compressed sketches. In: Proceedings of SISAP 2009, pp. 30–38. IEEE Computer Society (2009)

    Google Scholar 

  12. Novak, D., Batko, M., Zezula, P.: Metric index: an efficient and scalable solution for precise and approximate similarity search. Inf. Syst. 36(4), 721–733 (2011)

    Article  Google Scholar 

  13. Novak, D., Zezula, P.: Performance study of independent anchor spaces for similarity searching. Comput. J. 57(11), 1741–1755 (2014)

    Article  Google Scholar 

  14. Novak, D., Zezula, P.: Rank aggregation of candidate sets for efficient similarity search. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds.) DEXA 2014. LNCS, vol. 8645, pp. 42–58. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10085-2_4

    Google Scholar 

  15. Novak, D., Zezula, P.: PPP-codes for large-scale similarity searching. In: Hameurlain, A. (ed.) TLDKS XXIV. LNCS, vol. 9510, pp. 61–87. Springer, Heidelberg (2016). doi:10.1007/978-3-662-49214-7_2

    Chapter  Google Scholar 

  16. Skopal, T., Pokorny, J., Snasel, V.: PM-Tree: pivoting metric tree for similarity search in multimedia databases. In: Proceedings of ADBIS 2004, pp. 99–114 (2004)

    Google Scholar 

  17. Tellez, E.S., Chavez, E., Navarro, G.: Succinct nearest neighbor search. Inf. Syst. 38(7), 1019–1030 (2013)

    Article  Google Scholar 

  18. Wang, Z., Dong, W., Josephson, W., Lv, Q., Charikar, M., Li, K.: Sizing sketches: a rank-based analysis for similarity search. SIGMETRICS Perform. Eval. Rev. 35(1), 157–168 (2007)

    Article  Google Scholar 

  19. Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: the Metric Space Approach. Advances in Database Systems, vol. 32. Springer Science & Business Media, New York (2006)

    MATH  Google Scholar 

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Acknowledgements

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

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Authors and Affiliations

  1. Masaryk University, Brno, Czech Republic

    Vladimir Mic, David Novak & Pavel Zezula

Authors
  1. Vladimir Mic
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  2. David Novak
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  3. Pavel Zezula
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Corresponding author

Correspondence to Vladimir Mic .

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Editors and Affiliations

  1. CNRS–IRISA , Rennes, France

    Laurent Amsaleg

  2. National Institute of Informatics , Tokyo, Japan

    Michael E. Houle

  3. Ludwig-Maximilians-Universität München , München, Germany

    Erich Schubert

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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. https://doi.org/10.1007/978-3-319-46759-7_19

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  • DOI: https://doi.org/10.1007/978-3-319-46759-7_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46758-0

  • Online ISBN: 978-3-319-46759-7

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