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dc.contributor.authorPont Tuset, Jordi
dc.contributor.authorMarqués Acosta, Fernando
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2016-05-19T11:58:33Z
dc.date.issued2012
dc.identifier.citationPont, J., Marques, F. Supervised assessment of segmentation hierarchies. A: European Conference on Computer Vision. "Lecture Notes in Computer Science". Florència: Springer, 2012, p. 814-827.
dc.identifier.isbn978-3-642-33764-2
dc.identifier.urihttp://hdl.handle.net/2117/87191
dc.description.abstractThis paper addresses the problem of the supervised assessment of hierarchical region-based image representations. Given the large amount of partitions represented in such structures, the supervised assessment approaches in the literature are based on selecting a reduced set of representative partitions and evaluating their quality. Assessment results, therefore, depend on the partition selection strategy used. Instead, we propose to find the partition in the tree that best matches the ground-truth partition, that is, the upper-bound partition selection. We show that different partition selection algorithms can lead to different conclusions regarding the quality of the assessed trees and that the upper-bound partition selection provides the following advantages: 1) it does not limit the assessment to a reduced set of partitions, and 2) it better discriminates the random trees from actual ones, which reflects a better qualitative behavior. We model the problem as a Linear Fractional Combinatorial Optimization (LFCO) problem, which makes the upper-bound selection feasible and efficient.
dc.format.extent14 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
dc.subject.lcshImage segmentation
dc.titleSupervised assessment of segmentation hierarchies
dc.typeConference lecture
dc.subject.lemacImatges -- Segmentació
dc.contributor.groupUniversitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.identifier.doi10.1007/978-3-642-33765-9_58
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
drac.iddocument10770503
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorPont, J., Marques, F.
upcommons.citation.contributorEuropean Conference on Computer Vision
upcommons.citation.pubplaceFlorència
upcommons.citation.publishedtrue
upcommons.citation.publicationNameLecture Notes in Computer Science
upcommons.citation.startingPage814
upcommons.citation.endingPage827


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