dc.creatorDias, Zanoni
dc.creatorGoldenstein, Siome
dc.creatorRocha, Anderson
dc.date2013-Sep
dc.date2015-11-27T13:32:00Z
dc.date2015-11-27T13:32:00Z
dc.date.accessioned2018-03-29T01:18:14Z
dc.date.available2018-03-29T01:18:14Z
dc.identifierForensic Science International. v. 231, n. 1-3, p. 178-89, 2013-Sep.
dc.identifier1872-6283
dc.identifier10.1016/j.forsciint.2013.05.002
dc.identifierhttp://www.ncbi.nlm.nih.gov/pubmed/23890634
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/200748
dc.identifier23890634
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1300981
dc.descriptionIn the past few years, several near-duplicate detection methods appeared in the literature to identify the cohabiting versions of a given document online. Following this trend, there are some initial attempts to go beyond the detection task, and look into the structure of evolution within a set of related images overtime. In this paper, we aim at automatically identify the structure of relationships underlying the images, correctly reconstruct their past history and ancestry information, and group them in distinct trees of processing history. We introduce a new algorithm that automatically handles sets of images comprising different related images, and outputs the phylogeny trees (also known as a forest) associated with them. Image phylogeny algorithms have many applications such as finding the first image within a set posted online (useful for tracking copyright infringement perpetrators), hint at child pornography content creators, and narrowing down a list of suspects for online harassment using photographs.
dc.description231
dc.description178-89
dc.languageeng
dc.relationForensic Science International
dc.relationForensic Sci. Int.
dc.rightsfechado
dc.rightsCopyright © 2013 Elsevier Ireland Ltd. All rights reserved.
dc.sourcePubMed
dc.subjectDigital Forensics
dc.subjectImage Phylogeny
dc.subjectKinship Analysis
dc.subjectPhylogeny Trees
dc.titleToward Image Phylogeny Forests: Automatically Recovering Semantically Similar Image Relationships.
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución