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首页> 外文期刊>Expert Systems with Application >Finding landmarks within settled areas using hierarchical density-based clustering and meta-data from publicly available images
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Finding landmarks within settled areas using hierarchical density-based clustering and meta-data from publicly available images

机译：使用基于层次密度的聚类和来自公共可用图像的元数据在定居区域内查找地标

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The process of determining relevant landmarks within a certain region is a challenging task, mainly due to its subjective nature. Many of the current lines of work include the use of density-based clustering algorithms as the base tool for such a task, as they permit the generation of clusters of different shapes and sizes. However, there are still important challenges, such as the variability in scale and density. In this paper, we present two novel density-based clustering algorithms that can be applied to solve this: K-DBSCAN, a clustering algorithm based on Gaussian Kernels used to detect individual inhabited cores within regions; and V-DBSCAN, a hierarchical algorithm suitable for sample spaces with variable density, which is used to attempt the discovery of relevant landmarks in cities or regions. The obtained results are outstanding, since the system properly identifies most of the main touristic attractions within a certain region under analysis. A comparison with respect to the state-of-the-art show that the presented method clearly outperforms the current methods devoted to solve this problem. (C) 2019 Elsevier Ltd. All rights reserved.
机译：主要由于其主观性，确定特定区域内相关地标的过程是一项艰巨的任务。当前的许多工作包括将基于密度的聚类算法用作该任务的基础工具，因为它们允许生成不同形状和大小的聚类。但是，仍然存在重要的挑战，例如规模和密度的可变性。在本文中，我们提出了两种可用于解决此问题的基于密度的新颖聚类算法：K-DBSCAN，一种基于高斯核的聚类算法，用于检测区域内的单个居住核。 V-DBSCAN，一种适用于具有可变密度的样本空间的分层算法，用于尝试发现城市或区域中的相关地标。由于该系统可以正确识别正在分析的某个区域内的大多数主要旅游景点，因此获得的结果非常出色。与最新技术的比较表明，所提出的方法明显优于目前致力于解决该问题的方法。 （C）2019 Elsevier Ltd.保留所有权利。

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