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dc.contributor.authorChen, Y.-
dc.contributor.authorDick, A.-
dc.contributor.authorLi, X.-
dc.contributor.authorVan Den Hengel, A.-
dc.identifier.citationImage and Vision Computing, 2013; 31(12):935-948-
dc.description.abstractMany recent image retrieval methods are based on the "bag-of- words" (BoW) model with some additional spatial consistency checking. This paper proposes a more accurate similarity measurement that takes into account spatial layout of visual words in an offline manner. The similarity measurement is embedded in the standard pipeline of the BoW model, and improves two features of the model: i) latent visual words are added to a query based on spatial co-occurrence, to improve query recall; and ii) weights of reliable visual words are increased to improve the precision. The combination of these methods leads to a more accurate measurement of image similarity. This is similar in concept to the combination of query expansion and spatial verification, but does not require query time processing, which is too expensive to apply to full list of ranked results. Experimental results demonstrate the effectiveness of our proposed method on three public datasets. © 2013 Elsevier B.V.-
dc.description.statementofresponsibilityYanzhi Chen, Anthony Dick, Xi Li, Anton van den Hengel-
dc.publisherElsevier Science BV-
dc.rights© 2013 Elsevier B.V. All rights reserved.-
dc.subjectObject retrieval-
dc.subjectSpatial expansion-
dc.subjectVisual word re-weighting-
dc.titleSpatially aware feature selection and weighting for object retrieval-
dc.typeJournal article-
dc.identifier.orcidDick, A. [0000-0001-9049-7345]-
dc.identifier.orcidVan Den Hengel, A. [0000-0003-3027-8364]-
Appears in Collections:Aurora harvest 4
Computer Science publications

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