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

e-Archivo Repository

Show simple item record

dc.contributor.author Pla Sacristán, Eduardo
dc.contributor.author González Díaz, Iván
dc.contributor.author Martínez Cortés, Tomás
dc.contributor.author Díaz de María, Fernando
dc.date.accessioned 2020-09-18T10:25:55Z
dc.date.available 2021-06-01T23:00:04Z
dc.date.issued 2019-06-01
dc.identifier.bibliographicCitation Expert Systems with Applications, (2019), v. 123, pp.: 315-327.
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/10016/30832
dc.description.abstract 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.
dc.description.sponsorship This work has been partially supported by the National Grants RTC-2016-5305-7 and TEC-2017-84395-P of the Spanish Ministry of Economy and Competitiveness.
dc.format.extent 12
dc.language.iso eng
dc.publisher Elsevier
dc.rights © 2019 Elsevier Ltd. All rights reserved.
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Density-based clustering
dc.subject.other K-DBSCAN
dc.subject.other V-DBSCAN
dc.subject.other Hierarchical clustering
dc.subject.other Landmark detection
dc.subject.other Tourism
dc.title Finding landmarks within settled areas using hierarchical density-based clustering and meta-data from publicly available images
dc.type article
dc.description.status Publicado
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1016/j.eswa.2019.01.046
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. RTC-2016-5305-7
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 315
dc.identifier.publicationlastpage 327
dc.identifier.publicationtitle Expert Systems with Applications
dc.identifier.publicationvolume 123
dc.identifier.uxxi AR/0000023336
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.affiliation.dpto UC3M. Departamento de Teoría de la Señal y Comunicaciones
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Procesado Multimedia
 Find Full text

Files in this item

*Click on file's image for preview. (Embargoed files's preview is not supported)


The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record