Abstract
Similarity search for information retrieval on a variety of datasets relies on a notion of neighborhood, frequently using binary relationships such as the kNN approach. We suggest, however, that the notion of a neighbor must recognize higher-order relationship, to capture neighbors in all directions. Proximity graphs, such as the Relative Neighbor Graphs (RNG), use trinary relationships which capture the notion of direction and have been successfully used in a number of applications. However, the current algorithms for computing the RNG, despite widespread use, are approximate and not scalable. This paper proposes a hierarchical approach and novel type of graph, the Generalized Relative Neighborhood Graph (GRNG) for use in a pivot layer that then guides the efficient and exact construction of the RNG of a set of exemplars. It also shows how to extend this to a multi-layer hierarchy which significantly improves over the state-of-the-art methods which can only construct an approximate RNG.
The support of NSF award 1910530 is gratefully acknowledged.
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References
Chavez, E., et al.: Half-space proximal: a new local test for extracting a bounded dilation spanner of a unit disk graph. In: Anderson, J.H., Prencipe, G., Wattenhofer, R. (eds.) OPODIS 2005. LNCS, vol. 3974, pp. 235–245. Springer, Heidelberg (2006). https://doi.org/10.1007/11795490_19
Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.L.: Searching in metric spaces. ACM Comput. Surv. (CSUR) 33(3), 273–321 (2001)
Delaunay, B.: Sur la sphère vide. A la mémoire de Georges Voronoï. Bulletin de l’Académie des Sciences de l’URSS, pp. 793–800 (1934)
Dong, W., Moses, C., Li, K.: Efficient k-nearest neighbor graph construction for generic similarity measures. In: Proceedings of the 20th International Conference on World Wide Web, pp. 577–586 (2011)
Escalante, O., Pérez, T., Solano, J., Stojmenovic, I.: RNG-based searching and broadcasting algorithms over internet graphs and peer-to-peer computing systems. In: The 3rd ACS/IEEE International Conference on Computer Systems and Applications, p. 17. IEEE (2005)
Foster, C., Sevilmis, B., Kimia, B.: Generalized Relative Neighborhood Graph (GRNG) for Similarity Search. arXiv preprint (2022)
Fu, C., Xiang, C., Wang, C., Cai, D.: Fast approximate nearest neighbor search with the navigating spreading-out graph. Proc. VLDB Endow. 12(5), 461–474 (2019)
Gabriel, K.R., Sokal, R.R.: A new statistical approach to geographic variation analysis. Syst. Zool. 18(3), 259–278 (1969)
Goto, M., Ishida, R., Uchida, S.: Preselection of support vector candidates by relative neighborhood graph for large-scale character recognition. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 306–310 (2015)
Hacid, H., Yoshida, T.: Incremental neighborhood graphs construction for multidimensional databases indexing. In: Kobti, Z., Wu, D. (eds.) AI 2007. LNCS (LNAI), vol. 4509, pp. 405–416. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72665-4_35
Han, D., Han, C., Yang, Y., Liu, Y., Mao, W.: Pre-extracting method for SVM classification based on the non-parametric K-NN rule. In: 2008 19th International Conference on Pattern Recognition, pp. 1–4. IEEE (2008)
Jaromczyk, J.W., Toussaint, G.T.: Relative neighborhood graphs and their relatives. Proc. IEEE 80(9), 1502–1517 (1992)
Katajainen, J., Nevalainen, O., Teuhola, J.: A linear expected-time algorithm for computing planar relative neighbourhood graphs. Inf. Process. Lett. 25(2), 77–86 (1987)
Kirkpatrick, D.G., Radke, J.D.: A framework for computational morphology. In: Machine Intelligence and Pattern Recognition, vol. 2, pp. 217–248. Elsevier (1985)
Rayar, F., Barrat, S., Bouali, F., Venturini, G.: An approximate proximity graph incremental construction for large image collections indexing. In: Esposito, F., Pivert, O., Hacid, M.-S., Raś, Z.W., Ferilli, S. (eds.) ISMIS 2015. LNCS (LNAI), vol. 9384, pp. 59–68. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25252-0_7
Rayar, F., Barrat, S., Bouali, F., Venturini, G.: Incremental hierarchical indexing and visualisation of large image collections. In: 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (2016)
Rayar, F., Barrat, S., Bouali, F., Venturini, G.: A viewable indexing structure for the interactive exploration of dynamic and large image collections. ACM Trans. Knowl. Discov. Data (TKDD) 12(1), 1–26 (2018)
Rayar, F., Goto, M., Uchida, S.: CNN training with graph-based sample preselection: application to handwritten character recognition. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 19–24. IEEE (2018)
Supowit, K.J.: The relative neighborhood graph, with an application to minimum spanning trees. J. ACM (JACM) 30(3), 428–448 (1983)
Tellez, E.S., Ruiz, G., Chavez, E., Graff, M.: Local search methods for fast near neighbor search. arXiv preprint arXiv:1705.10351 (2017)
Toussaint, G.T.: The relative neighbourhood graph of a finite planar set. Pattern Recogn. 12(4), 261–268 (1980)
de Vries, N.J., Arefin, A.S., Mathieson, L., Lucas, B., Moscato, P.: Relative neighborhood graphs uncover the dynamics of social media engagement. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q.Z. (eds.) ADMA 2016. LNCS (LNAI), vol. 10086, pp. 283–297. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49586-6_19
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Foster, C., Sevilmis, B., Kimia, B. (2022). Generalized Relative Neighborhood Graph (GRNG) for 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_11
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