Finding landmarks within settled areas using hierarchical density-based clustering and meta-data from publicly available images

• Published in: Expert systems with applications Vol. 123; pp. 315 - 327
• Main Authors: Pla-Sacristán, Eduardo, González-Díaz, Iván, Martínez-Cortés, Tomás, Díaz-de-María, Fernando
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.06.2019; Elsevier BV
• Subjects: Algorithms; Art exhibits; Clustering; Density; Density-based clustering; Hierarchical clustering; K-DBSCAN; Landmark detection; Landmarks; Tourism; V-DBSCAN; Tourism; Hierarchical clustering; Density-based clustering; V-DBSCAN; Landmark detection; K-DBSCAN
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.01.046

•Automatic tools for touristic applications are highly valuable for the user•Single-scale clustering methods are insufficient to solve real clustering problems•Multi-scale (hierarchical) density-based clustering improves landmark detection•The separation of inhabited population cores facilitates the clustering approach•Increasing the dimensionality improves the results within crowded sample spaces. 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.