Abstract
For decades, the success of the similarity search has been based on a detailed quantification of pairwise similarity of objects. Currently, the search features have become much more precise but also bulkier, and the similarity computations more time-consuming. While the k nearest neighbours (kNN) search dominates the real-life applications, we claim that it is principally free of a need for precise similarity quantifications. Based on the well-known fact that a selection of the most similar alternative out of several options is a much easier task than deciding the absolute similarity scores, we propose the search based on an epistemologically simpler concept of relational similarity. Having arbitrary objects \(q, o_1, o_2\) from the search domain, the kNN search is solvable just by the ability to choose the more similar object to q out of \(o_1, o_2\) – the decision can also contain a neutral option. We formalise such searching and discuss its advantages concerning similarity quantifications, namely its efficiency and robustness. We also propose a pioneering implementation of the relational similarity search for the Euclidean spaces and report its extreme filtering power in comparison with 3 contemporary techniques.
This research was supported by ERDF “CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence” (No. CZ.02.1.01/0.0/0.0/16_019/ 0000822).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Amato, G., Falchi, F., Vadicamo, L.: Visual recognition of ancient inscriptions using convolutional neural network and fisher vector. ACM J. Comput. Cultural Heritage 9(4), 21:1–21:24 (2016)
Brázdil, J.: Dimensionality reduction methods for vector spaces. Master’s thesis, Masaryk University, Faculty of Informatics, Brno (2016). https://is.muni.cz/th/v9xlg/. Supervisor Pavel Zezula
Chang, R.: Are Hard Cases Vague Cases? Value Incommensurability: Ethics, Risk, and Decision-Making, pp. 50–70. Routledge, New York (2021)
Deza, M.M., Deza, E.: Encyclopedia of Distances, pp. 1–583. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00234-2
Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: Proceedings of the 31th International Conference on Machine Learning, ICML, China, pp. 647–655 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012)
Mariachkina, I.: Experimental verification of a synergy of techniques for efficient similarity search in metric spaces (2022). https://is.muni.cz/th/m14as/. Bachelor’s thesis, Masaryk University, Faculty of Informatics, Brno, supervisor Vladimir Mic
Mic, V., Novak, D., Zezula, P.: Designing sketches for similarity filtering. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 655–662 (2016)
Mic, V., Novak, D., Zezula, P.: Sketches with unbalanced bits for similarity search. In: Beecks, C., Borutta, F., Kröger, P., Seidl, T. (eds.) SISAP 2017. LNCS, pp. 53–63. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-319-68474-1_4
Mic, V., Novak, D., Zezula, P.: Binary sketches for secondary filtering. ACM Trans. Inf. Syst. 37(1), 1:1–1:28 (2018)
Novak, D., Zezula, P.: PPP-codes for large-scale similarity searching. Trans. Large-Scale Data- and Knowl.-Centered Syst. 24, 61–87 (2016)
Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. Series 6 2(11), 559–572 (1901)
Sedmidubský, J., Elias, P., Zezula, P.: Effective and efficient similarity searching in motion capture data. Multimed. Tools Appl. 77(10), 12073–12094 (2018)
Skopal, T., Bustos, B.: On nonmetric similarity search problems in complex domains. ACM Comput. Surv. 43(4), 34:1–34:50 (2011)
Skopal, T., Durisková, D., Pechman, P., Dobranský, M., Khachaturian, V.: Videolytics: system for data analytics of video streams. In: ACM International Conference on Information and Knowledge Management (CIKM), Australia, pp. 4794–4798. ACM (2021)
Wall, M.E., Rechtsteiner, A., Rocha, L.M.: Singular value decomposition and principal component analysis. In: Berrar, D.P., Dubitzky, W., Granzow, M. (eds.) A Practical Approach to Microarray Data Analysis, pp. 91–109. Springer, Heidelberg (2003). https://doi.org/10.1007/0-306-47815-3_5
Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search - The Metric Space Approach, vol. 32 (2006). https://doi.org/10.1007/0-387-29151-2
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mic, V., Zezula, P. (2022). Concept of Relational Similarity Search. In: Skopal, T., Falchi, F., Lokoč, J., Sapino, M.L., Bartolini, I., Patella, M. (eds) Similarity Search and Applications. SISAP 2022. Lecture Notes in Computer Science, vol 13590. Springer, Cham. https://doi.org/10.1007/978-3-031-17849-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-17849-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17848-1
Online ISBN: 978-3-031-17849-8
eBook Packages: Computer ScienceComputer Science (R0)