Neighborhood Matching for Image Retrieval

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dc.contributor.author González Díaz, Iván
dc.contributor.author Birinci, Murat
dc.contributor.author Díaz de María, Fernando
dc.contributor.author Delp, Edward J.
dc.date.accessioned 2020-09-21T09:43:56Z
dc.date.available 2020-09-21T09:43:56Z
dc.date.issued 2017-03-01
dc.identifier.bibliographicCitation IEEE Transactions on Multimedia, (2017), 19(3), pp. 544-558.
dc.identifier.issn 1520-9210
dc.identifier.uri http://hdl.handle.net/10016/30837
dc.description.abstract In the last few years, large-scale image retrieval has attracted a lot of attention from the multimedia community. Usual approaches addressing this task first generate an initial ranking of the reference images using fast approximations that do not take into consideration the spatial arrangement of local features in the image (e.g., the bag-of-words paradigm). The top positions of the rankings are then re-estimatedwith verificationmethods that deal with more complex information, such as the geometric layout of the image. This verification step allows pruning of many false positives at the expense of an increase in the computational complexity, whichmay prevent its application to large-scale retrieval problems. This paper describes a geometric method known as neighborhood matching (NM), which revisits the keypointmatching process by considering a neighborhood around each keypoint and improves the efficiency of a geometric verification step in the image search system. Multiple strategies are proposed and compared to incorporate NM into a large-scale image retrieval framework. A detailed analysis and comparison of these strategies and baseline methods have been investigated. The experiments show that the proposed method not only improves the computational efficiency, but also increases the retrieval performance and outperforms state-of-the-artmethods in standard datasets, such as the Oxford 5 k and 105 k datasets, for which the spatial verification step has a significant impact on the system performance.
dc.description.sponsorship This work was supported in part by the National Grant TEC2014-53390-P and National Grant TEC2014-61729-EXP of the Spanish Ministry of Economy and Competitiveness
dc.format.extent 14
dc.language.iso eng
dc.publisher IEEE
dc.rights © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.
dc.subject.other Geometric verification
dc.subject.other Image retrieval
dc.subject.other Neighborhood matching (NM)
dc.subject.other Robust estimation
dc.title Neighborhood Matching for Image Retrieval
dc.type article
dc.description.status Publicado
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TMM.2016.2616298
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2014-53390-P
dc.relation.projectID Gobierno de España. TEC2014-61729-EXP
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 544
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 558
dc.identifier.publicationtitle IEEE Transactions on Multimedia
dc.identifier.publicationvolume 19
dc.identifier.uxxi AR/0000019452
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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