Fast visual vocabulary construction for image retrieval using skewed-split k-d trees

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• dc.contributor.author Gialampoukidis, Iliasca
• dc.contributor.author Vrochidis, Stefanosca
• dc.contributor.author Kompatsiaris, Ioannisca
• dc.date.accessioned 2016-12-05T08:36:14Z
• dc.date.available 2017-01-03T03:00:06Z
• dc.date.issued 2016ca
• dc.description Comunicació presentada a la 22nd International Conference on MultiMedia Modeling (MMM16), celebrada els dies 4 a 6 de gener 2016 a Miami (FL, EUA).ca
• dc.description.abstract Most of the image retrieval approaches nowadays are based on the Bag-of-Words (BoW) model, which allows for representing an image efficiently and quickly. The efficiency of the BoW model is related to the efficiency of the visual vocabulary. In general, visual vocabularies are created by clustering all available visual features, formulating specific patterns. Clustering techniques are k-means oriented and they are replaced by approximate k-means methods for very large datasets. In this work, we propose a faster construction of visual vocabularies compared to the existing method in the case of SIFT descriptors, based on our observation that the values of the 128-dimensional SIFT descriptors follow the exponential distribution. The application of our method to image retrieval in specific image datasets showed that the mean Average Precision is not reduced by our approximation, despite that the visual vocabulary has been constructed significantly faster compared to the state of the art methods.en
• dc.description.sponsorship This work was supported by the projects MULTISENSOR (FP7-610411) and KRISTINA (H2020-645012), funded by the European Commission.en
• dc.format.mimetype application/pdfca
• dc.identifier.citation Gialampoukidis I, Vrochidis S, Kompatsiaris I. Fast visual vocabulary construction for image retrieval using skewed-split k-d trees. In: Tian Q, Sebe N, Qi GJ, Huet B, Hong R, Liu X, editors. 22nd International Conference on MultiMedia Modeling (MMM16); 2016 gen.4-6; Miami (FL, USA). [place unknown]: Springer; 2016. Part 1, Poster papers; p. 466-477. DOI: 10.1007/978-3-319-27671-7_39ca
• dc.identifier.doi http://dx.doi.org/10.1007/978-3-319-27671-7_39
• dc.identifier.uri http://hdl.handle.net/10230/27692
• dc.language.iso engca
• dc.publisher Springerca
• dc.relation.ispartof Tian Q, Sebe N, Qi GJ, Huet B, Hong R, Liu X, editor. 22nd International Conference on MultiMedia Modeling (MMM16); 2016 gen.4-6; Miami (FL, USA). [S.l.]: Springer; 2016. Part 1, Poster papers; p. 466-477.
• dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/610411ca
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/645012
• dc.rights © Springer The final publication is available at Springer via/nhttp://dx.doi.org/10.1007/978-3-319-27671-7_39.ca
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.other Recuperació de la informacióca
• dc.subject.other Imatges -- Processamentca
• dc.title Fast visual vocabulary construction for image retrieval using skewed-split k-d treesca
• dc.type info:eu-repo/semantics/conferenceObjectca
• dc.type.version info:eu-repo/semantics/acceptedVersionca