Multimodal retrieval with relevance feedback based on genetic programming

dc.creatorCalumby, RT
dc.creatorTorres, RD
dc.creatorGoncalves, MA
dc.date2014
dc.dateAPR
dc.date2014-08-01T18:41:15Z
dc.date2015-11-26T17:07:41Z
dc.date2014-08-01T18:41:15Z
dc.date2015-11-26T17:07:41Z
dc.date.accessioned2018-03-28T23:56:12Z
dc.date.available2018-03-28T23:56:12Z
dc.identifierMultimedia Tools And Applications. Springer, v. 69, n. 3, n. 991, n. 1019, 2014.
dc.identifier1380-7501
dc.identifier1573-7721
dc.identifierWOS:000333209300019
dc.identifier10.1007/s11042-012-1152-7
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/82165
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/82165
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1280176
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionThis paper presents a framework for multimodal retrieval with relevance feedback based on genetic programming. In this supervised learning-to-rank framework, genetic programming is used for the discovery of effective combination functions of (multimodal) similarity measures using the information obtained throughout the user relevance feedback iterations. With these new functions, several similarity measures, including those extracted from different modalities (e.g., text, and content), are combined into one single measure that properly encodes the user preferences. This framework was instantiated for multimodal image retrieval using visual and textual features and was validated using two image collections, one from the Washington University and another from the ImageCLEF Photographic Retrieval Task. For this image retrieval instance several multimodal relevance feedback techniques were implemented and evaluated. The proposed approach has produced statistically significant better results for multimodal retrieval over single modality approaches and superior effectiveness when compared to the best submissions of the ImageCLEF Photographic Retrieval Task 2008.
dc.description69
dc.description3
dc.description991
dc.description1019
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFAPESP [2007/52015-0, 2009/18438-7]
dc.descriptionCNPq [57.3871/2008-6]
dc.languageen
dc.publisherSpringer
dc.publisherDordrecht
dc.publisherHolanda
dc.relationMultimedia Tools And Applications
dc.relationMultimed. Tools Appl.
dc.rightsfechado
dc.rightshttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dc.sourceWeb of Science
dc.subjectMultimodal retrieval
dc.subjectLearn to rank
dc.subjectImage retrieval
dc.subjectRelevance feedback
dc.subjectGenetic programming
dc.subjectImage Retrieval
dc.subjectPhotographic Retrieval
dc.subjectTexture Descriptors
dc.subjectColor
dc.subjectAnnotation
dc.subjectSimilarity
dc.subjectShape
dc.subjectCorrelograms
dc.subjectRanking
dc.subjectSearch
dc.titleMultimodal retrieval with relevance feedback based on genetic programming
dc.typeArtículos de revistas


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