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Pivot Selection for Narrow Sketches by Optimization Algorithms

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12440)

Abstract

Sketches are compact bit strings that are considered as products of an LSH for high-dimensional data. We use them in filtering for narrowing down solution candidates in similarity search. We propose a pivot selection method for narrow sketches with a length such as 16-bits by optimization algorithms with the accuracy of filtering itself as the objective function.

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References

  1. Budikova, P., Batko, M., Zezula, P.: Evaluation platform for content-based image retrieval systems. In: Gradmann, S., Borri, F., Meghini, C., Schuldt, H. (eds.) TPDL 2011. LNCS, vol. 6966, pp. 130–142. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24469-8_15

    CrossRef  Google Scholar 

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

    Google Scholar 

  3. Higuchi, N., Imamura, Y., Kuboyama, T., Hirata, K., Shinohara, T.: Nearest neighbor search using sketches as quantized images of dimension reduction. In: Proceedings of the ICPRAM 2018, pp. 356–363 (2018)

    Google Scholar 

  4. Higuchi, N., Imamura, Y., Kuboyama, T., Hirata, K., Shinohara, T.: Fast filtering for nearest neighbor search by sketch enumeration without using matching. In: Liu, J., Bailey, J. (eds.) AI 2019. LNCS (LNAI), vol. 11919, pp. 240–252. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35288-2_20

    CrossRef  Google Scholar 

  5. Higuchi, N., Imamura, Y., Kuboyama, T., Hirata, K., Shinohara, T.: Fast nearest neighbor search with narrow 16-bit sketch. In: Proceedings of the ICPRAM 2019, pp. 540–547 (2019)

    Google Scholar 

  6. Higuchi, N., Imamura, Y., Shinohara, T., Hirata, K., Kuboyama, T.: Annealing by increasing resampling. In: De Marsico, M., Sanniti di Baja, G., Fred, A. (eds.) ICPRAM 2019. LNCS, vol. 11996, pp. 71–92. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-40014-9_4

    CrossRef  Google Scholar 

  7. Imamura, Y., Higuchi, N., Kuboyama, T., Hirata, K., Shinohara, T.: Pivot selection for dimension reduction using annealing by increasing resampling. In: Proceedings of the LWDA 2017, pp. 15–24 (2017)

    Google Scholar 

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

    Google Scholar 

  9. Mic, V., Novak, D., Zezula, P.: Designing sketches for similarity filtering. In: ICDMW 2016, pp. 655–662 (2016)

    Google Scholar 

  10. Mic, V., Novak, D., Zezula, P.: Speeding up similarity search by sketches. In: Proceeding of the SISAP 2016, pp. 250–258 (2016)

    Google Scholar 

  11. Mic, V.: Binary Sketches for Similarity Search. Doctoral thesis, Masaryk University, Faculty of Informatics, Brno (2020)

    Google Scholar 

  12. Müller, A., Shinohara, T.: Efficient similarity search by reducing i/o with compressed sketches. In: Proceedings of the SISAP 2009, pp. 30–38 (2009)

    Google Scholar 

  13. Novak, D., Batko, M., Zezula, P.: Large-scale image retrieval using neural net descriptors. In: Proceedings of the SIGIR 2015, pp. 1039–1040 (2015)

    Google Scholar 

  14. Shinohara, T., Ishizaka, H.: On dimension reduction mappings for approximate retrieval of multi-dimensional data. In: Arikawa, S., Shinohara, A. (eds.) Progress in Discovery Science. LNCS (LNAI), vol. 2281, pp. 224–231. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45884-0_14

    CrossRef  Google Scholar 

  15. Wang, Z., Dong, W., Josephson, W., Lv, Q., Charikar, M., Li, K.: Sizing sketches: a rank-based analysis for similarity search. In: Proceedings of the ACM SIGMETRICS 2007, pp. 157–168 (2007)

    Google Scholar 

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Acknowledgments

This research was partly supported by ERDF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16_019/0000822), and also by JSPS KAKENHI Grant Numbers 17H00762, 19K12125, 19H01133, and 20H00595.

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Correspondence to Takeshi Shinohara .

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Higuchi, N., Imamura, Y., Mic, V., Shinohara, T., Hirata, K., Kuboyama, T. (2020). Pivot Selection for Narrow Sketches by Optimization Algorithms. In: Satoh, S., et al. Similarity Search and Applications. SISAP 2020. Lecture Notes in Computer Science(), vol 12440. Springer, Cham. https://doi.org/10.1007/978-3-030-60936-8_3

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  • DOI: https://doi.org/10.1007/978-3-030-60936-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60935-1

  • Online ISBN: 978-3-030-60936-8

  • eBook Packages: Computer ScienceComputer Science (R0)