A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

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dc.contributor.authorGuimarães Pedronette, Daniel Carlos [UNESP]
dc.contributor.authorCalumby, Rodrigo T.
dc.contributor.authorTorres, Ricardo da S.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2018-12-11T16:39:00Z
dc.date.available2018-12-11T16:39:00Z
dc.date.issued2015-12-11
dc.description.abstractThe interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semi-supervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.en
dc.description.affiliationDepartment of Statistics, Applied Mathematics and Computing - State University of São Paulo (UNESP)
dc.description.affiliationRecod Lab - Institute of Computing, University of Campinas (UNICAMP)
dc.description.affiliationDepartment of Exact Sciences, University of Feira de Santana (UEFS), BA
dc.description.affiliationUnespDepartment of Statistics, Applied Mathematics and Computing - State University of São Paulo (UNESP)
dc.identifierhttp://dx.doi.org/10.1186/s13640-015-0081-6
dc.identifier.citationEurasip Journal on Image and Video Processing, v. 2015, n. 1, 2015.
dc.identifier.doi10.1186/s13640-015-0081-6
dc.identifier.file2-s2.0-84938879619.pdf
dc.identifier.issn1687-5281
dc.identifier.issn1687-5176
dc.identifier.scopus2-s2.0-84938879619
dc.identifier.urihttp://hdl.handle.net/11449/167952
dc.language.isoeng
dc.relation.ispartofEurasip Journal on Image and Video Processing
dc.relation.ispartofsjr0,409
dc.relation.ispartofsjr0,409
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectCollaborative image retrieval
dc.subjectContent-based image retrieval
dc.subjectRecommendation
dc.subjectRelevance feedback
dc.subjectSemi-supervised learning
dc.titleA semi-supervised learning algorithm for relevance feedback and collaborative image retrievalen
dc.typeArtigo

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