Adaptive and fast image superpixel segmentation approach

Wang, Nannan; Zhang, Yongxia

doi:10.1016/j.imavis.2021.104315

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2022

2024

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“…This clusters X into "high density" regions. Density based clustering has received interest from many fields because of its applicability to a vast range of real world problems [1,16,5,18,2,15].…”

Section: Introductionmentioning

confidence: 99%

Geometric reconstructions of density based clusterings

Garcia-Pulido¹,

Samardzhiev²

2022

Preprint

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DBSCAN * and HDBSCAN * are well established density based clustering algorithms. However, obtaining the clusters of very large datasets is infeasible, limiting their use in real world applications.By exploiting the geometry of Euclidean space, we prove that it is possible to systematically construct the DBSCAN * and HDBSCAN * clusters of a finite X ⊂ R n from specific subsets of X. We are able to control the size of these subsets and therefore our results make it possible to cluster very large datasets.To illustrate our theory, we cluster the Microsoft Building Footprint Database of the US, which is not possible using the standard implementations.

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