Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/342023
Full metadata record
DC FieldValueLanguage
dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbn978-1-4799-8131-1pt_BR
dc.contributor.authorunicampOliveira, Alberto Arruda de-
dc.contributor.authorunicampRocha, Anderson de Rezende-
dc.typeArtigopt_BR
dc.titleScore-based learning for relevance prediction in image similarity searchpt_BR
dc.contributor.authorOliveira, Alberto-
dc.contributor.authorRocha, Anderson-
dc.subjectOrganização da informaçãopt_BR
dc.subjectConjunto de dadospt_BR
dc.subject.otherlanguageOrganization of informationpt_BR
dc.subject.otherlanguageData setspt_BR
dc.description.abstractPredicting the performance of queries when labels are not present has been a recurring problem faced in information retrieval systems. Beyond its clear importance, it can also be applied to aid post-retrieval optimization approaches such as re-ranking or rank-aggregation. However, most post-retrieval performance prediction approaches to retrieval systems rely on generating a single effectiveness value of performance for queries. We propose an alternative method to assess the performance of systems reliant on similarity search, which consists of predicting the individual relevance of ranked results according to the distribution of similarity scores of a given query compared to instances in a collection. The idea is that relationships between the ith ranked score and other scores of the rank can be leveraged to generate features which, in turn, are used to classify ranked objects according to their relevance to the query. We propose a positional classification scheme, in conjunction with simple and fast score-based features to predict the relevance of the top-10 results of a similarity search rank. Our results in nine scenarios, comprising three different large image datasets, show good prediction accuracy for the top-10 results, with the advantage of being amenable suitable to deploy at query timept_BR
dc.relation.ispartofIEEE international conference on acoustics, speech and signal processing. proceedingspt_BR
dc.relation.ispartofabbreviationICASSPpt_BR
dc.publisher.cityPiscataway, NJpt_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherInstitute of Electrical and Electronics Engineerspt_BR
dc.date.issued2019-
dc.date.monthofcirculationApr.pt_BR
dc.language.isoengpt_BR
dc.rightsFechadopt_BR
dc.sourceSCOPUSpt_BR
dc.identifier.issn2379-190Xpt_BR
dc.identifier.eissn1520-6149pt_BR
dc.identifier.doi10.1109/ICASSP.2019.8682519pt_BR
dc.identifier.urlhttps://ieeexplore.ieee.org/document/8682519pt_BR
dc.date.available2020-05-22T17:11:21Z-
dc.date.accessioned2020-05-22T17:11:21Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2020-05-22T17:11:21Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-08-27T19:18:07Z : No. of bitstreams: 1 2-s2.0-85068999647.pdf: 412599 bytes, checksum: e382875d349cb2be4fd1ca4d6c037e82 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-05-22T17:11:21Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/342023-
dc.description.conferencenomeICASSP 2019 - 2019 IEEE international conference on acoustics, speech and signal processing (ICASSP)pt_BR
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordAudio signal processingpt_BR
dc.subject.keywordForecastingpt_BR
dc.subject.keywordLarge datasetpt_BR
dc.subject.keywordOptimization approachpt_BR
dc.identifier.source2-s2.0-85068999647pt_BR
dc.creator.orcid0000-0002-4711-2777pt_BR
dc.creator.orcid0000-0002-4236-8212pt_BR
dc.type.formArtigo de eventopt_BR
dc.identifier.articleid18777856pt_BR
Appears in Collections:IC - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-85068999647.pdf402.93 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.