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Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression

Mostra el registre parcial de l'element Gialampoukidis, Ilias Moumtzidou, Anastasia Liparas, Dimitris Tsikrika, Theodora Vrochidis, Stefanos Kompatsiaris, Ioannis 2017-09-08T15:15:30Z 2017
dc.identifier.citation Gialampoukidis I, Moumtzidou A, Liparas D, Tsikrika T, Vrochidis S, Kompatsiaris I. Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression. Multimed Tools Appl. 2017 May 15. [21 p.]. DOI: 10.1007/s11042-017-4797-4
dc.identifier.issn 1380-7501
dc.description.abstract Heterogeneous sources of information, such as images, videos, text and metadata are often used to describe di erent or complementary views of the same multimedia object, especially in the online news domain and in large annotated image collections. The retrieval of multimedia objects, given a mul- timodal query, requires the combination of several sources of information in an e cient and scalable way. Towards this direction, we provide a novel unsuper- vised framework for multimodal fusion of visual and textual similarities, which are based on visual features, visual concepts and textual metadata, integrating non-linear graph-based fusion and Partial Least Squares Regression. The fu- sion strategy is based on the construction of a multimodal contextual similarity matrix and the non-linear combination of relevance scores from query-based similarity vectors. Our framework can employ more than two modalities and high-level information, without increase in memory complexity, when com- pared to state-of-the-art baseline methods. The experimental comparison is done in three public multimedia collections in the multimedia retrieval task. The results have shown that the proposed method outperforms the baseline methods, in terms of Mean Average Precision and Precision@20.
dc.description.sponsorship This work was partially supported by the European Commission by the projects MULTISENSOR (FP7-610411) and KRISTINA (H2020-645012).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Multimedia Tools and Applications. 2017 May 15. [21 p.].
dc.rights © Springer The final publication is available at Springer via
dc.title Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression
dc.type info:eu-repo/semantics/article
dc.subject.keyword Multimedia retrieval
dc.subject.keyword Non-linear fusion
dc.subject.keyword Graph-based models
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/610411
dc.rights.accessRights info:eu-repo/semantics/embargoedAccess
dc.type.version info:eu-repo/semantics/acceptedVersion
dc.embargo.liftdate 2018-05-15 info:eu-repo/date/embargoEnd/2018-05-15


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